Quality of reporting in abstracts of RCTs published in emergency medicine journals: a systematic survey of the literature suggests we can do better
Bibliographic record
Abstract
OBJECTIVE: We investigated the association between the publication of the Consolidated Standards of Reporting Trials extension for abstracts (CONSORT-EA) and other variables of interest on the quality of reporting of abstracts of randomised controlled trials (RCTs) published in emergency medicine (EM) journals. METHODS: We performed a survey of the literature, comparing the quality of reporting before (2005-2007) with after (2014-2015) the publication of the dedicated CONSORT-EA in 2008. The quality of reporting was measured as the sum of items of the CONSORT-EA checklist reported in each abstract, ranging from 0 to 15. The main explanatory variable was the period of publication: pre-CONSORT-EA versus post-CONSORT-EA public. Other explanatory variables were journal's endorsement of the CONSORT statement, number of centres participating in the study, study's sample size, type of intervention, significance of results, source of funding and study setting. We analysed the data using generalised estimation equations, performing a univariate and a multivariable analysis. RESULTS: We retrieved 844 articles, and randomly selected 60 per period for review, after stratifying for journal. The mean (SD) number of items reported was 6.4 (1.9) in the period before and 6.9 (1.8) in the period after the publication of the CONSORT-EA, with an adjusted mean difference (aMD) of 0.47 (95% CI -0.13 to 1.06). Abstracts of trials of pharmacological interventions had a significantly larger mean number of reported items than those of trials of non-pharmacological interventions (aMD 1.59; 95% CI 0.94 to 2.24). CONCLUSIONS: The quality of reporting in abstracts of RCTs published in EM journals is low and was not significantly impacted by the publication of a dedicated CONSORT-EA.
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 | 0.438 | 0.813 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.015 |
| Bibliometrics | 0.018 | 0.024 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".