Getting this right from the start will ensure findings from clinical trials can be used effectively
Bibliographic record
Abstract
Clinical trials produce research that is intended to add to medical knowledge and improve the way we treat patients. Unfortunately, trials of the same condition don't always report the same outcomes, making synthesis of results from different studies challenging. A core outcome set (COS) is a condition-specific set of outcomes, which should be agreed and established as a minimum reporting standard for all clinical trials of interventions for that condition. This will ensure that the results can contribute to future data synthesis. Additionally, use of COS will minimise reporting biases by standardising the outcomes that are reported for a condition. A systematic review investigating caesarean rates in trials of induction of labour identified 157 trials with 31,085 included patients (Mishanina et al. CMAJ Canadian Medical Association Journal 2014;186:665–73). All of the trials reported rates of caesarean section but only 20 trials reported maternal mortality rates. Synthesis of the data for the rate of caesarean section gave a result with narrow confidence intervals (RR 0.88, 95% CI 0.84–0.93) but because the maternal mortality data was scarce and because this outcome is rare, the synthesis of the results gave a wide confidence interval (RR 1.00, 95% CI 0.10–9.57). Had all 157 trials reported maternal mortality as a core outcome, synthesis of these results would have been more likely to have produced a useful result that would be able to inform clinical guidelines. COS development has been driven by the COMET (Core Outcome Measures in Effectiveness Trials) initiative. One of the most important aspects of a COS is that all potential stakeholders are involved during the development, to ensure that the final set of outcomes is relevant to the people whom the interventions directly or indirectly target; not only researchers but health professionals, policy makers and most importantly patients and their relatives. It is envisaged that in the future, a COS will exist for all medical conditions. When designing a clinical trial, researchers should search the COMET database to check whether a COS exists for their condition of interest and if it does they should use the specified outcome in their trial or justify why they have chosen not to. NAMC is an editor for the series but was excluded from the peer-review process of this article. Full disclosure of interests available to view online as supporting information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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 teacher head, 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".