Developing a core outcome set for clinical trials in olfactory disorders: a COMET initiative
Bibliographic record
Abstract
STATEMENT OF PROBLEM: Evaluating the effectiveness of the management of Olfactory Dysfunction (OD) has been limited by a paucity of high-quality randomised and/or controlled trials. A major barrier is heterogeneity of outcomes in such studies. Core outcome sets (COS) - standardized sets of outcomes that should be measured/reported as determined by consensus-would help overcome this problem and facilitate future meta-analyses and/or systematic reviews (SRs). We set out to develop a COS for interventions for patients with OD. METHODS: 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 patients and healthcare practitioners to individually rate the outcomes in terms of importance on a 9-point Likert scale. RESULTS: After 2 rounds of the iterative eDelphi process, the initial outcomes were distilled down to a final COS including subjective questions (visual analogue scores, quantitative and qualitative), quality of life measures, psychophysical testing of smell, baseline psychophysical testing of taste, and presence of side effects along with the investigational medicine/device and patient's symptom log. CONCLUSIONS: Inclusion of these core outcomes in future trials will increase the value of research on clinical interventions for OD. We include recommendations regarding the outcomes that should be measured, although future work will be 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 | 0.806 | 0.811 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.027 | 0.021 |
| Open science | 0.011 | 0.041 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".