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Record W3033943032 · doi:10.1136/bmj.m1714

Evaluating the credibility of anchor based estimates of minimal important differences for patient reported outcomes: instrument development and reliability study

2020· article· en· W3033943032 on OpenAlexafffund
Tahira Devji, Alonso Carrasco‐Labra, Anila Qasim, Mark Phillips, Bradley C. Johnston, Niveditha Devasenapathy, Dena Zeraatkar, Meha Bhatt, Xuejing Jin, Romina Brignardello‐Petersen, Olivia Urquhart, Farid Foroutan, Stefan Schandelmaier, Héctor Pardo‐Hernández, Robin W.M. Vernooij, Hsiaomin Huang, Yamna Rizwan, Reed Siemieniuk, Lyubov Lytvyn, Donald L. Patrick, Shanil Ebrahim, Toshi A. Furukawa, Gihad Nesrallah, Holger J. Schünemann, Mohit Bhandari, Lehana Thabane, Gordon Guyatt

Bibliographic record

VenueBMJ · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsSt. Michael's HospitalWestern UniversityUniversity of GuelphUniversity of AlbertaDalhousie UniversityHumber River Regional HospitalMcMaster UniversityImpact
FundersCanadian Institutes of Health Research
KeywordsCredibilityPsycINFOReliability (semiconductor)CINAHLMEDLINERating scaleInter-rater reliabilityPsychologyIntraclass correlationMedicinePsychometricsApplied psychologyPhysical therapyClinical psychologyPsychological interventionPsychiatryDevelopmental psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: To develop an instrument to evaluate the credibility of anchor based minimal important differences (MIDs) for outcome measures reported by patients, and to assess the reliability of the instrument. DESIGN: Instrument development and reliability study. DATA SOURCES: Initial criteria were developed for evaluating the credibility of anchor based MIDs based on a literature review (Medline, Embase, CINAHL, and PsycInfo databases) and the experience of the authors in the methodology for estimation of MIDs. Iterative discussions by the team and pilot testing with experts and potential users facilitated the development of the final instrument. PARTICIPANTS: With the newly developed instrument, pairs of masters, doctoral, or postdoctoral students with a background in health research methodology independently evaluated the credibility of a sample of MID estimates. MAIN OUTCOME MEASURES: Core credibility criteria applicable to all anchor types, additional criteria for transition rating anchors, and inter-rater reliability coefficients were determined. RESULTS: The credibility instrument has five core criteria: the anchor is rated by the patient; the anchor is interpretable and relevant to the patient; the MID estimate is precise; the correlation between the anchor and the outcome measure reported by the patient is satisfactory; and the authors select a threshold on the anchor that reflects a small but important difference. The additional criteria for transition rating anchors are: the time elapsed between baseline and follow-up measurement for estimation of the MID is optimal; and the correlations of the transition rating with the baseline, follow-up, and change score in the patient reported outcome measures are satisfactory. Inter-rater reliability coefficients (ĸ) for the core criteria and for one item from the additional criteria ranged from 0.70 to 0.94. Reporting issues prevented the evaluation of the reliability of the three other additional criteria for the transition rating anchors. CONCLUSIONS: Researchers, clinicians, and healthcare policy decision makers can consider using this instrument to evaluate the design, conduct, and analysis of studies estimating anchor based minimal important differences.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.335
metaresearch head score (Gemma)0.653
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.820

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3350.653
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0070.007
Science and technology studies0.0010.003
Scholarly communication0.0040.004
Open science0.0020.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.494
GPT teacher head0.514
Teacher spread0.020 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designBench or experimental
DomainMethods
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations270
Published2020
Admission routes2
Has abstractyes

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