How to select outcome measurement instruments for outcomes included in a “Core Outcome Set” – a practical guideline
Bibliographic record
Abstract
BACKGROUND: In cooperation with the Core Outcome Measures in Effectiveness Trials (COMET) initiative, the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) initiative aimed to develop a guideline on how to select outcome measurement instruments for outcomes (i.e., constructs or domains) included in a "Core Outcome Set" (COS). A COS is an agreed minimum set of outcomes that should be measured and reported in all clinical trials of a specific disease or trial population. METHODS: Informed by a literature review to identify potentially relevant tasks on outcome measurement instrument selection, a Delphi study was performed among a panel of international experts, representing diverse stakeholders. In three consecutive rounds, panelists were asked to rate the importance of different tasks in the selection of outcome measurement instruments, to justify their choices, and to add other relevant tasks. Consensus was defined as being achieved when 70 % or more of the panelists agreed and when fewer than 15 % of the panelists disagreed. RESULTS: Of the 481 invited experts, 120 agreed to participate of whom 95 (79 %) completed the first Delphi questionnaire. We reached consensus on four main steps in the selection of outcome measurement instruments for COS: Step 1, conceptual considerations; Step 2, finding existing outcome measurement instruments, by means of a systematic review and/or a literature search; Step 3, quality assessment of outcome measurement instruments, by means of the evaluation of the measurement properties and feasibility aspects of outcome measurement instruments; and Step 4, generic recommendations on the selection of outcome measurement instruments for outcomes included in a COS (consensus ranged from 70 to 99 %). CONCLUSIONS: This study resulted in a consensus-based guideline on the methods for selecting outcome measurement instruments for outcomes included in a COS. This guideline can be used by COS developers in defining how to measure core outcomes.
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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 | 0.205 | 0.287 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.017 | 0.008 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".