A Call for Evidence-based Decision Making When Selecting Outcome Measurement Instruments for Summary of Findings Tables in Systematic Reviews: Results from an OMERACT Working Group
Bibliographic record
Abstract
OBJECTIVE: Systematic reviews often struggle with how to combine information when more than 1 instrument is used across studies being synthesized. Different techniques have been suggested based on frequency of use in the literature, or on consensus. We explore an approach blending 2 initiatives: OMERACT (Outcome Measurement in Rheumatology) and COSMIN (Consensus On Selection of Measurement Instruments), and investigate the effects of an evidence-based measurement approach on selection of outcomes. METHODS: Readings were circulated to attendees registered for a preconference workshop on pain measurement. Three instruments were considered and exercises conducted to engage people in the content and measurement performance of these tools. Consensus was sought that an evidence-based approach could be created for selection of instruments for summary of findings (SoF) tables. RESULTS: The blending of COSMIN and OMERACT approaches led to an evidence-based approach that depended both on a clear definition of target concept and a review of measurement performance of the instrument. Participants emphasized that conceptual clarity and practical considerations should come before measurement property results. CONCLUSION: Evidence-based approaches can be adopted for selection of instruments for SoF tables. A research agenda was formulated.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.847 | 0.881 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.021 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.030 | 0.044 |
| Open science | 0.014 | 0.031 |
| Research integrity | 0.026 | 0.037 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".