Prognosis filters for MEDLINE: performance data
Prognosis filters have, to date, not been reviewed using systematic review approaches. In the absence of a formal systematic review it is difficult to gain an overall picture of the comparative performance of the different filters. The figures on this page present the performance data as reported in the published filter articles for:
· The performance of a MEDLINE filter as reported by the filter authors
· The performance of a MEDLINE filter as reported by other researchers who have validated the performance of the filter(s).
Prognosis filters mostly aim to identify three main types of study:
· General prognosis filters seeking to identify all types of prognosis study
· Prognostic factors
· Prognostic models
The data tables are here.
The filters are at the bottom of this page.
How to cite this page:
Glanville J, Lefebvre C, Manson P, Robinson S, Shaw N. Prognosis filters for MEDLINE: performance data [internet]. York (UK): The InterTASC Information Specialists' Sub-Group; 2006 [updated 11 Feb. 2022; cited 11 Feb. 2022]. Available from: https://sites.google.com/a/york.ac.uk/issg-search-filters-resource/home/prognosis-performance-data
General prognosis filters for MEDLINE
This figure shows that the most sensitive general prognosis filter as reported by authors (blue bars) was Ingui (98.2%), followed by the Haynes clinical prediction rules broad strategy (96%) and Teljeur-Murphy 26 (95%). All but one of the filters which have been validated in other articles showed a drop-off in performance in the validation studies, except for Kavanagh 2020 which showed an increase from 82% to 90%. Amongst validation studies, the best performing filters (orange bars) are the Yale filter (100%), and the Yale-1 filter (94%) (note that these may be the same filter - it is difficult to tell from the articles).
All other filters that were validated, performed below 95% sensitivity.
This figure shows that the most precise general prognosis filter as reported by authors (blue bars) was Teljeur-Murphy 22 (5%), followed by the Hedges specific strategy (3%). Four filters reported precision of 2%. The Teljeur-Murphy filters 22 and 26 showed a much better precision when used in validation exercises (both achieved 6%). Other filters (which had not been validated by their authors or for which the authors did not report precision data ) showed high precision in validation studies: Geersing (10%), Ingui (6%), Yale-2 (6%) and Yale -1 (5%).
Prognostic factors filters for MEDLINE
This figure shows that the most sensitive filter as reported by authors (blue bars) was Ingui (98.2%), followed by the Haynes broad strategy (96%) and Stallings (95%). The filters that have been validated in other articles, all show a drop-off in performance in the validation studies. Amongst validation studies, the best performing filters (orange bars) are Irvin (90.4%) and the Parker Hedges+natural history filter (90.1%). None of the filters that were validated reached 95% sensitivity.
This figure shows that the most precise filter as reported by authors (blue bars) was the Wilczynski optimised filter (2%). This filter, when validated in other sets of studies (orange bars), showed a large increase in precision to 20.6%. The Wilczynski optimised filter experienced only a small drop in sensitivity in the validation set (see above), which does not explain the large increase in precision.
Among filters that have only have precision data reported in validation studies, Irvin (18.3%) and the two Parker filters were the most precise (17.7% and 20.2%).
Prognostic models filters for MEDLINE
The most sensitive filter (see below) according to authors’ reported results (blue bars) was the Ingui filter (98.2%), followed by the Ebell “modified Haynes 26 ” filter (96.4%), the Haynes broad filter (96%) and Teljeur-Murphy 26 (95%).
In the validation studies (orange bars), the Ingui OR Geersing filter when validated in a second gold standard had a 97% sensitivity.
Two filters were validated more than once. The Ingui filter was validated three times with performance varying between 78% and 100%. The Haynes broad filter was validated four times and all of the validation scores were lower than the authors’ reported performance (between 76% and 94%).
The most precise filter according to authors’ reported results (blue bars) was the McGrath-Murphy narrow filter (25%), followed by Haynes narrow filter (12%), the Ebell RCSI filter (9.9%), and the McGrath-Murphy broad filter (9%). In the validation studies (orange bars), the Haynes narrow filter had an increased precision (13%).
Two filters had more than one validation study. The Ingui filter had precision of 1.85% and 0.97% in two studies. The Haynes broad filter had precision between 0.27% and 2 % in three different validations.
Searchers seeking a highly sensitive strategy will experience lower levels of precision. This is clearly demonstrated in these filters. The Ingui filter had a sensitivity of 98.2% but low precision (between 0.97% and 1.85%). The McGrath Murphy narrow conversely has relatively high precision (25%) but low sensitivity (39%).
The next figure shows the highest sensitivity and precision data for filters that provided authors’ data on both measures. The data are only shown for filters with sensitivity of 94% or greater.
Key:
(1: Only sensitivity data reported)
2: Ebell “modified Haynes 26”
3: Haynes broad
4. Teljeur-Murphy 26
5: Ebell RCSI filter
6: Ingui OR Geersing
Filters
Irvin
Cohort Studies/
incidence.tw.
Mortality/
Follow-Up Studies/
prognos*.tw.
predict*.tw.
course.tw.
Survival Analysis/
or/1-8
Reported in:
Irvin E, Hayden J. Developing and testing an optimal search strategy for identifying studies of prognosis. Presented at: 14th Cochrane Colloquium; Dublin, Ireland; 23–26 Oct 2006
Validated in:
Boulos L, Ogilvie R, Hayden J A. Search methods for prognostic factor systematic reviews: a methodologic investigation. J Med Lib Assoc [Online]. 2021; 109(1): 23–32.
Hedges prognosis sensitive/ broad (Clinical queries)
incidence [MeSH: noexp] OR mortality [MeSH Terms] OR follow up studies[MeSH: noexp] OR prognos*[Text Word] OR predict*[Text Word] OR course*[Text Word]
Reported in:
Wilczynski NL, Haynes RB. Developing optimal search strategies for detecting clinically sound prognostic studies in MEDLINE: an analytic survey. BMC Med. 2004;2:23. [PubMed].
and
Clinical queries filters. National Library of Medicine; 2 Nov 2021. https://pubmed.ncbi.nlm.nih.gov/help/#clinical-study-category-filters [Last accessed: 9 Dec 2022]
Validated in:
Kavanagh PL, Frater F, Navarro T, LaVita P, Parrish R, Iorio A, Optimizing a literature surveillance strategy to retrieve sound overall prognosis and risk assessment model papers, J Am Med Inform Assoc 2021; ocaa232.
Frazier JJ, Stein CD, Tseytlin E, and Bekhuis T. Building a gold standard to construct search filters: a case study with biomarkers for oral cancer. J Med Libr Assoc. 2015; 103(1): 22–30.
Stallings, E., Gaetano-Gil, A., Alvarez-Diaz, N. et al. Development and evaluation of a search filter to identify prognostic factor studies in Ovid MEDLINE. BMC Med Res Methodol 2022;22:107.
Hedges prognosis specific/ narrow (Clinical queries)
(prognos*[Title/Abstract] OR (first[Title/Abstract] AND episode[Title/Abstract]) OR cohort[Title/Abstract])
Reported in:
Wilczynski NL, Haynes RB. Developing optimal search strategies for detecting clinically sound prognostic studies in MEDLINE: an analytic survey. BMC Med. 2004;2:23. [PubMed].
and
Clinical queries filters. National Library of Medicine; 2 Nov 2021. https://pubmed.ncbi.nlm.nih.gov/help/#clinical-study-category-filters [Last accessed: 9 Dec 2022]
Valdated in: no validation identified.
Wilczynski optimised
prognosis.sh.
diagnosed.tw.
cohort:.mp.
predictor:.tw.
death.tw.
exp models, statistical/
or/1-6
Reported in:
Wilczynski NL, Haynes RB. Developing optimal search strategies for detecting clinically sound prognostic studies in MEDLINE: an analytic survey. BMC Med. 2004;2:23. [PubMed].
Validated in:
Boulos L, Ogilvie R, Hayden J A. Search methods for prognostic factor systematic reviews: a methodologic investigation. J Med Lib Assoc [Online]. 2021; 109(1): 23–32.
Haynes broad or sensitive Clinical Prediction Guides strategy
(Predict*[tiab) OR Predictive value Of tests[mh] OR Scor*[tiab] OR Observ*[tiab] OR Observer variation[mh]
Reported in:
Clinical queries filters. National Library of Medicine; 2 Nov 2021. https://pubmed.ncbi.nlm.nih.gov/help/#clinical-study-category-filters [Last accessed: 9 Dec 2022]
Validated in:
Keogh C, Wallace E, O'Brien KK, Murphy PJ, Teljeur C, McGrath B, Smith SM, Doherty N, Dimitrov BD, Fahey T. Optimized retrieval of primary care clinical prediction rules from MEDLINE to establish aWeb-based register. J Clin Epi. 2011;64(8):848-60.
Geersing GJ, Bouwmeester W, Zuithoff P, Spijker R, Leeflang M, Moons K. Search filters for finding prognostic and diagnostic prediction studies in Medline to enhance systematic reviews. PLoS One. 2012;7(2):e32844
Haynes narrow or specific Clinical Prediction Guides strategy
validation [tiab] OR validate [tiab]
Reported in:
Clinical queries filters. National Library of Medicine; 2 Nov 2021. https://pubmed.ncbi.nlm.nih.gov/help/#clinical-study-category-filters [Last accessed: 9 Dec 2022]
Validated in:
Keogh C, Wallace E, O'Brien KK, Murphy PJ, Teljeur C, McGrath B, Smith SM, Doherty N, Dimitrov BD, Fahey T. Optimized retrieval of primary care clinical prediction rules from MEDLINE to establish aWeb-based register. J Clin Epi. 2011;64(8):848-60.
Ebell MH, Fahey T, Murphy ME, Barry A, Barry H, Hickner J. An updated and more efficient search strategy to identify primary care relevant clinical prediction rules. J Clin Epidemiol. 2020; 125: 26-29 doi:10.1016/j.jclinepi.2020.05.013
Parker inclusive general
cohort.ti,ab.
incidence.ti,ab.
mortality.ti,ab.
follow-up study.ti,ab.
follow-up studies.ti,ab.
prognos*.ti,ab.
predict*.ti,ab.
course.ti,ab.
natural history.ti,ab.
or/1-9
Reported in:
Parker R, Tougas ME, Hayden JA. Validating prognosis search filters using relative recall based on prognosis systematic reviews. Poster presented at: 21st Cochrane Colloquium; Quebec City, QC, Canada; 19–23 Sep 2013
Validated in:
Boulos L, Ogilvie R, Hayden J A. Search methods for prognostic factor systematic reviews: a methodologic investigation. J Med Lib Assoc [Online]. 2021; 109(1): 23–32.
Parker - Hedges+
natural history
Incidence/
exp Mortality/
Follow-Up Studies/
prognos*.tw.
predict*.tw.
course*.tw.
(first and episode).ti,ab.
cohort.ti,ab.
natural history.tw.
or/1-9
Reported in:
Parker R, Tougas ME, Hayden JA. Validating prognosis search filters using relative recall based on prognosis systematic reviews. Poster presented at: 21st Cochrane Colloquium; Quebec City, QC, Canada; 19–23 Sep 2013
Validated in:
Boulos L, Ogilvie R, Hayden J A. Search methods for prognostic factor systematic reviews: a methodologic investigation. J Med Lib Assoc [Online]. 2021; 109(1): 23–32.
Geersing GJ, Bouwmeester W, Zuithoff P, Spijker R, Leeflang M, Moons K. Search filters for finding prognostic and diagnostic prediction studies in Medline to enhance systematic reviews. PLoS One. 2012;7(2):e32844
Ingui
(Validat$ OR Predicts.ti. OR Rules OR (PredictS AND (Outcomes OR Risk$ OR ModelS)) OR (History OR Variable$ OR Criteria OR Scors OR Characteristic$ OR Finding$ OR Factors) AND (Predicts OR Models OR Decisions OR IdentifS OR Prognoss» OR (Decisions AND (ModelS OR Clinical$ OR Logistic Models')) OR (Prognostic AND (History OR VariableS OR Criteria OR Scor$ OR CharacteristicS OR FindingS OR FactorS OR Model$))
Reported in:
Ingui BJ, Rogers MA. Searching for clinical prediction rules in MEDLINE. J Am Med Inform Assoc. 2001;8(4):391-7.
Validated in:
Geersing GJ, Bouwmeester W, Zuithoff P, Spijker R, Leeflang M, Moons K. Search filters for finding prognostic and diagnostic prediction studies in Medline to enhance systematic reviews. PLoS One. 2012;7(2):e32844
Kavanagh PL, Frater F, Navarro T, LaVita P, Parrish R, Iorio A, Optimizing a literature surveillance strategy to retrieve sound overall prognosis and risk assessment model papers, J Am Med Inform Assoc 2021; ocaa232.
Ingui high sensitivity
Predict$ OR Risk$
Reported in:
Ingui BJ, Rogers MA. Searching for clinical prediction rules in MEDLINE. J Am Med Inform Assoc. 2001;8(4):391-7.
Validated in:
Wong SS, Wilczynski NL, Haynes RB, Ramkissoonsingh R, Hedges Team. Developing optimal search strategies for detecting sound clinical prediction studies in MEDLINE. AMIA Annual Symposium Proceedings 2003;728-32.
Ingui high sensitivity and high specificity
Decision Support Techniques/ AND Predictive Value of Tests
Reported in:
Ingui BJ, Rogers MA. Searching for clinical prediction rules in MEDLINE. J Am Med Inform Assoc. 2001;8(4):391-7.
Validated in:
Wong SS, Wilczynski NL, Haynes RB, Ramkissoonsingh R, Hedges Team. Developing optimal search strategies for detecting sound clinical prediction studies in MEDLINE. AMIA Annual Symposium Proceedings 2003;728-32.
Geersing
"stratification" OR "ROC curve" [mesh] Or "discrimination" OR "discriminate" OR "c-statistic" OR "area under the curve" OR "auc" OR "calibration" OR "indices" OR "algorithm" OR "multivariable"
This is reported combined with Ingui or Haynes broad filters using the OR operator
Reported and validated in:
Geersing GJ, Bouwmeester W, Zuithoff P, Spijker R, Leeflang M, Moons K. Search filters for finding prognostic and diagnostic prediction studies in Medline to enhance systematic reviews. PLoS One. 2012;7(2):e32844
Teljeur-Murphy 26 inclusion filter
(“clinical prediction” OR clinical model*[All Fields] OR clinical score*[All Fields] OR decision rule*[All Fields] OR diagnostic accuracy OR diagnostic rule*[All Fields] OR diagnostic score*[All Fields] OR diagnostic value OR predictive outcome*[All Fields] OR predictive rule*[All Fields] OR predictive score*[All Fields] OR predictive value OR predictive risk*[All Fields] OR prediction outcome*[All Fields] OR prediction rule*[All Fields] OR prediction score*[All Fields] OR prediction value*[All Fields] OR prediction risk*[All Fields] OR risk assessment OR risk score*[All Fields] OR validation decision*[All Fields] OR validation rule*[All Fields] OR validation score*[All Fields] OR (derivation AND validation) OR (sensitivity AND specificity))
Reported in:
Keogh C, Wallace E, O'Brien KK, Murphy PJ, Teljeur C, McGrath B, Smith SM, Doherty N, Dimitrov BD, Fahey T. Optimized retrieval of primary care clinical prediction rules from MEDLINE to establish aWeb-based register. J Clin Epi. 2011;64(8):848-60.
Validated in:
Kavanagh PL, Frater F, Navarro T, LaVita P, Parrish R, Iorio A, Optimizing a literature surveillance strategy to retrieve sound overall prognosis and risk assessment model papers, J Am Med Inform Assoc 2021; ocaa232.
Keogh C, Wallace E, O'Brien KK, Murphy PJ, Teljeur C, McGrath B, Smith SM, Doherty N, Dimitrov BD, Fahey T. Optimized retrieval of primary care clinical prediction rules from MEDLINE to establish aWeb-based register. J Clin Epi. 2011;64(8):848-60.
Teljeur-Murphy 22
(clinical[tiab] AND predict*[tiab]) OR (clinical[tiab] AND model*[tiab] ) OR (clinical[tiab] AND score*[tiab]) OR (decision [tiab] AND rule*[tiab]) OR (derive*[tiab] AND validat*[tiab]) OR (diagnos*[tiab] AND accura*[tiab]) OR (diagnos*[tiab] AND rule*[tiab]) OR (diagnos*[tiab] AND score*[tiab]) OR (diagnos*[tiab] AND value[tiab]) OR (predict*[tiab] AND outcome*[tiab]) OR (predict*[tiab] AND rule*[tiab] OR (predict*[tiab] AND score*[tiab] ) OR (predict*[tiab] AND validat*[tiab]) OR (predict*[tiab] AND value*[tiab]) OR (risk*[tiab] AND assessment*[tiab]) OR (risk[tiab] AND score*[tiab]) OR (sensitivity[tiab] AND specificity[tiab]) OR (symptoms[tiab] AND signs[tiab]) OR (validat*[tiab] AND decision*[tiab]) OR (validat*[tiab] AND rule*[tiab]) OR (validat*[tiab] AND score*[tiab]) OR (predict*[tiab] AND risk*[tiab])
Reported in:
Keogh C, Wallace E, O'Brien KK, Murphy PJ, Teljeur C, McGrath B, Smith SM, Doherty N, Dimitrov BD, Fahey T. Optimized retrieval of primary care clinical prediction rules from MEDLINE to establish aWeb-based register. J Clin Epi. 2011;64(8):848-60.
Teljeur-Murphy exclusion
(allele OR amino OR animal OR apoptosis OR chromosome OR congenital OR dental OR dna OR endogenous OR endothelial OR epithelial OR mammalian OR mice OR molecule OR molecular OR mouse OR mutate OR mutation OR necrosis OR pathogenesis OR phosphorylation OR polymorphism OR receptor OR signal OR species OR tissue OR tumor OR tumour OR tyrosine OR vitro)
Reported in:
Keogh C, Wallace E, O'Brien KK, Murphy PJ, Teljeur C, McGrath B, Smith SM, Doherty N, Dimitrov BD, Fahey T. Optimized retrieval of primary care clinical prediction rules from MEDLINE to establish aWeb-based register. J Clin Epi. 2011;64(8):848-60
Yale prognosis and natural history (best terms)
cohort studies[mh] or prognosis[mh] or disease progression[mh]
Reported on the Yale web site [Last accessed: 9 Dec 2022]
Validated in:
Kok R, Verbeek JAHM, Faber B, van Dijk, Frank JH, Hoving, Jan L A search strategy to identify studies on the prognosis of work disability: a diagnostic test framework. BMJ Open. 2015;5:e006315. doi:10.1136/bmjopen-2014-006315
although it is unclear if the filter validated was this one of the Yale prognosis and natural history below.
Yale prognosis and natural history
cohort studies[mh] OR prognosis[mh] OR mortality[mh] OR morbidity[mh] OR "natural history" OR prognost*[tiab] OR course[tiab] OR predict*[tiab] OR outcome assessment[mh] OR outcome*[tiab] OR inception cohort* OR disease progression[mh] OR survival analysis[mh]
Reported on the Yale web site [Last accessed: 9 Dec 2022]
Validated in:
Kok R, Verbeek JAHM, Faber B, van Dijk, Frank JH, Hoving, Jan L A search strategy to identify studies on the prognosis of work disability: a diagnostic test framework. BMJ Open. 2015;5:e006315. doi:10.1136/bmjopen-2014-006315
although it is unclear if the filter validated was this one or the Yale filter above.
Kavanagh
prognos*[TIAB] OR cohort [TIAB] OR validat*[TIAB] OR predict* [TIAB] OR mortality [TIAB] OR follow up [TIAb]
Reported in:
Kavanagh PL, Frater F, Navarro T, LaVita P, Parrish R, Iorio A, Optimizing a literature surveillance strategy to retrieve sound overall prognosis and risk assessment model papers, J Am Med Inform Assoc 2021; ocaa232.
Validated in: none reported
Ebell RCSI filter for clinical prediction rules
((“clinical prediction”[tiab] OR “clinical model”[tiab] OR “clinical score”[tiab] OR “clinical scoring”[tiab] OR “validation of a clinical”[tiab] OR “decision guideline”[tiab] OR “validation study”[tiab] OR “validation studies”[tiab] OR “derivation study”[tiab] OR “screening score”[tiab] OR “decision rule”[tiab] OR “diagnostic rule”[tiab] OR “diagnostic score”[tiab] OR “predictive outcome”[tiab] OR “predictive rule”[tiab] OR “predictive score”[tiab] OR “predictive value”[tiab] OR “predictive risk”[tiab] OR “prediction outcome”[tiab] OR “prediction rule”[tiab] OR “prediction score”[tiab] OR “scoring”[tiab] OR “prediction value”[tiab] OR “prediction risk”[tiab] OR “risk assessment”[tiab] OR “risk score”[tiab] OR “risk scoring”[tiab] OR “prognostic score”[tiab] OR “prognostic index”[tiab] OR “prognostic rule”[tiab] OR “prospective validation”[tiab] OR (“risk”[tiab] AND “tool”[tiab]) OR ((“validate”[tiab] OR “validation”[tiab] OR “validating”[tiab] OR “develop”[tiab] OR “development”[tiab] OR “derivation”[tiab] OR “derive”[tiab] OR “deriving”[tiab] OR “performance”[tiab]) AND (“decision”[tiab] OR “predictive”[tiab] OR “prediction”[tiab] OR “rule”[tiab] OR "score"[tiab] OR “scoring”[tiab] OR “index”[tiab] OR “model”[tiab] OR “scale”[tiab] OR “tool”[tiab] OR “algorithm”[tiab])) OR (“development”[tiab] AND “validation”[tiab]) OR (“derivation”[tiab] AND “validation”[tiab]) OR “signs and symptoms”[tiab]) OR (((“PISA”[tiab] OR “PERC”[tiab] OR “PESI”[tiab] OR “Geneva”[tiab] OR “Wells”[tiab]) AND “pulmonary embolism”[tiab]) OR (“Leiden”[tiab] AND “rheumatoid”[tiab]) OR “Ottawa ankle”[tiab] OR “Ottawa knee”[tiab] OR (“Wells”[tiab] AND (“thrombosis”[tiab] OR "thromboembolism"[tiab])) OR ((“ATRIA stroke”[tiab] OR “ATRIA score”[tiab] OR “ATRIA risk”[tiab] OR “ATRIA bleeding”[tiab] OR “HAS-BLED”[tiab]) AND (“atrial fibrillation”[tiab] OR “anticoagulation”[tiab] OR “anticoagulant”[tiab])) OR ((“Centor”[tiab] OR “FeverPAIN”[tiab]) AND (“sore throat”[tiab] OR “pharyngitis”[tiab])) OR ((“AUDIT score”[tiab] OR “CRAFFT”[tiab] OR “AUDIT rule”[tiab] OR “AUDIT score”[tiab] OR “CAGE”[tiab]) AND (“alcohol”[tiab] OR “alcoholism”[tiab])) OR (“CAPRA”[tiab] AND “prostate”[tiab]) OR “San Francisco Syncope Rule”[tiab] OR ((“MMSE”[tiab] OR “MiniCog”[tiab] OR “Montreal Cognitive Assessment”[tiab]) AND (“dementia”[tiab] or “cognitive impairment”[tiab])) OR ((“ABCD2”[tiab] OR “ABCD”[tiab]) AND “transient ischemic attack”[tiab]) OR ((“CHADS2”[tiab] OR “CHA2DS2-VASc”[tiab] OR “CHADS-VASC”[tiab]) AND “atrial fibrillation”[tiab]) OR ((“PHQ-2”[tiab] OR “PHQ-7”[tiab] OR “two question screener”[tiab]) AND “depression”[tiab]) OR ((“GAD-2”[tiab] OR “GAD-7”[tiab]) AND “anxiety”[tiab]) OR “PC-PTSD”[tiab] OR “CRB65”[tiab] OR “CRB-65”[tiab] OR (“C-WATCH”[tiab] AND (“bleeding” OR “haemorrhage”[tiab])) OR “BAP-65”[tiab] OR “STARWAVE”[tiab] OR “CURB-65”[tiab] OR “CURB65”[tiab]))
Reported in:
Ebell MH, Fahey T, Murphy ME, Barry A, Barry H, Hickner J. An updated and more efficient search strategy to identify primary care relevant clinical prediction rules. J Clin Epidemiol. 2020; 125: 26-29 doi:10.1016/j.jclinepi.2020.05.013
Validated in: none reported
Ebell modified Haynes 26
((validation[tiab] OR validate[tiab]) OR (“clinical prediction” OR clinical model*[All Fields] OR clinical score*[All Fields] OR decision rule*[All Fields] OR diagnostic accuracy OR diagnostic rule*[All Fields] OR diagnostic score*[All Fields] OR diagnostic value OR predictive outcome*[All Fields] OR predictive rule*[All Fields] OR predictive score*[All Fields] OR predictive value OR predictive risk*[All Fields] OR prediction outcome*[All Fields] OR prediction rule*[All Fields] OR prediction score*[All Fields] OR prediction value*[All Fields] OR prediction risk*[All Fields] OR risk assessment OR risk score*[All Fields] OR validation decision*[All Fields] OR validation rule*[All Fields] OR validation score*[All Fields] OR (derivation AND validation) OR (sensitivity AND specificity))
Reported in:
Ebell MH, Fahey T, Murphy ME, Barry A, Barry H, Hickner J. An updated and more efficient search strategy to identify primary care relevant clinical prediction rules. J Clin Epidemiol. 2020; 125: 26-29 doi:10.1016/j.jclinepi.2020.05.013
Validated in: none reported
Hayden
cohort, incidence, mortality, follow-up studies, prognos*, predict*, course
Reported in:
McKibbon K, Eady A, Marks S. Evidence Based Principles and Practice.PDQ Series. Hamilton, Canada: BC Dekker; 1999
Validated in:
Hayden JA, Côté P, Bombardier C. Evaluation of the quality of prognosis studies in systematic reviews. Ann Intern Med. 2006 Mar 21;144(6):427-37. doi: 10.7326/0003-4819-144-6-200603210-00010.
Wong best balance
predict:.tw. OR validat:.tw. OR develop.tw
Reported in:
Wong SS, Wilczynski NL, Haynes RB, Ramkissoonsingh R, Hedges Team. Developing optimal search strategies for detecting sound clinical prediction studies in MEDLINE. AMIA Annual Symposium Proceedings 2003;728-32.
Validated in: none reported
McGrath-Murphy broad
((predict* N3 rule* OR predict* N3 model OR predict* N3 models) OR (decision* N3 rule*) OR (TX validat*))
Reported in:
Keogh C, Wallace E, O'Brien KK, Murphy PJ, Teljeur C, McGrath B, Smith SM, Doherty N, Dimitrov BD, Fahey T. Optimized retrieval of primary care clinical prediction rules from MEDLINE to establish aWeb-based register. J Clin Epi. 2011;64(8):848-60.
Validated in: none reported
McGrath-Murphy narrow
((predict* N3 rule* OR predict* N3 model OR predict* N3 models) OR (decision* N3 rule*))
Reported in:
Keogh C, Wallace E, O'Brien KK, Murphy PJ, Teljeur C, McGrath B, Smith SM, Doherty N, Dimitrov BD, Fahey T. Optimized retrieval of primary care clinical prediction rules from MEDLINE to establish aWeb-based register. J Clin Epi. 2011;64(8):848-60.
Validated in: none reported
Stallings
1 exp Risk/
2 risk.tw
3 exp Cohort Studies/
4 cohort.tw
5 exp Prognosis/
6 "prognos*".tw
7 "predict*".tw
8 exp Incidence/
9 incidence.tw
10 exp Survival Analysis/
11 survival.tw
12 "causal factor".tw
13 course.tw
14 or/1–13
Reported in:
Stallings, E., Gaetano-Gil, A., Alvarez-Diaz, N. et al. Development and evaluation of a search filter to identify prognostic factor studies in Ovid MEDLINE. BMC Med Res Methodol 2022;22:107.
Validated in: none reported
How to cite this page:
Glanville J, Lefebvre C, Manson P, Robinson S, Shaw N. Prognosis filters for MEDLINE: performance data [internet]. York (UK): The InterTASC Information Specialists' Sub-Group; 2006 [updated 1 July 2022; cited 1 July 2022]. Available from: https://sites.google.com/a/york.ac.uk/issg-search-filters-resource/home/prognosis-performance-data