Editorial: The relevance of core outcome sets to clinical guideline development
Bibliographic record
Abstract
A core outcome set (COS) is 'an agreed standard set of outcomes that should be measured and reported, as a minimum in trials for a specific health condition', 1 developed via consensus with stakeholders.Although the focus on COS originated on randomised trials, their use in systematic reviews, routine care, audit and, importantly, in clinical guidelines, is being increasingly recognised.The core outcome measures in effectiveness trials (COMET) initiative is an organisation for anyone interested in the development and application of COS and they host a database of studies relating to COS (https://comet-initiative.org/Resources/Database).There are at least three main reasons why we would advocate the use of a relevant COS when developing a clinical guideline.First, there is the desire to have a research ecosystem where this minimum set of outcomes considered and reported in trials, combined in systematic reviews and used for clinical decision-making and monitoring patient progress are the same.Consistency will reduce bias and increase efficiency, and COS are a means to facilitating this.Second, it reduces duplication of effort.COS developers go through a rigorous, transparent process and involve all relevant parties including patients and members of the public in determining the outcomes of critical importance; 1,2 guideline authors commonly repeat an almost identical procedure.Third, using COS in guidelines would encourage researchers to consider using them when designing trials.The International Guideline Development Credentialing and Certification Programme (https://inguide.org/)mentions COS and some guideline organisations,
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 | 0.021 | 0.143 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.027 | 0.027 |
| Insufficient payload (model declined to judge) | 0.019 | 0.018 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".