CHronic Rhinosinusitis Outcome MEasures (CHROME), developing a core outcome set for trials of interventions in chronic rhinosinusitis
Bibliographic record
Abstract
STATEMENT OF PROBLEM: Evaluating the effectiveness of treatments in chronic rhinosinusitis (CRS) have been limited by both a paucity of high quality randomised trials, and the heterogeneity of outcomes in those that have been reported. Core outcome sets (COS) are an agreed, standardized set of outcomes that should be measured and reported by future trials as a minimum and will facilitate future meta-analysis of trial results in systematic reviews (SRs). We set out to develop a core outcome set for interventions for adults with CRS. METHOD(S) OF STUDY: A long-list of potential outcomes was identified by a steering group utilising a literature review, thematic analysis of a wide range of stakeholders' views and systematic analysis of currently available Patient Reported Outcome Measures (PROMs). A subsequent e-Delphi process allowed 110 patients and healthcare practitioners to individually rate the outcomes in terms of importance, on a Likert scale. MAIN RESULTS: After 2 rounds of the iterative Delphi process, the 54 initial outcomes were distilled down to a final core-outcome set of 15 items, over 4 domains. PRINCIPAL CONCLUSIONS: The authors hope inclusion of these core outcomes in future trials will increase the value of research on interventions for CRS in adults. It was felt important to make recommendations regarding how these outcomes should be measured, although additional work is now required to further develop and revalidate existing outcome measures.
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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 | 0.464 | 0.583 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".