Reporting Quality of Journal Abstracts for Surgical Randomized Controlled Trials Before and After the Implementation of the CONSORT Extension for Abstracts
Bibliographic record
Abstract
BACKGROUND: Adequate reporting is crucial in full-text publications but even more so in abstracts because they are the most frequently read part of a publication. In 2008, an extension for abstracts of the Consolidated Standards of Reporting Trials (CONSORT-A) statement was published, defining which items should be reported in abstracts of randomized controlled trials (RCTs). Therefore, we compared the adherence of RCT abstracts to CONSORT-A before and after the publication of CONSORT-A. METHODS: RCTs published in the five surgical journals with the highest impact factor were identified through PubMed for 2005-2007 and 2014-2016. Adherence to 15 CONSORT-A items and two additional items for abstracts of non-pharmacological trials was assessed in duplicate. We compared the overall adherence to CONSORT-A between the two time periods using an unpaired t test and explored adherence to specific items. RESULTS: A total of 192 and 164 surgical RCT abstracts were assessed (2005-2007 and 2014-2016, respectively). In the pre-CONSORT-A phase, the mean score of adequately reported items was 6.14 (95% confidence interval [CI] 5.90-6.38) and 8.11 in the post-CONSORT-A phase (95% CI 7.83-8.39; mean difference 1.97, 95% CI 1.60-2.34; p < 0.0001). The comparison of individual items indicated a significant improvement in 9 of the 15 items. The three least reported items in the post-CONSORT-A phase were randomization (2.4%), blinding (13.4%), and funding (0.0%). Specific items for non-pharmacological trials were rarely reported (approximately 10%). CONCLUSION: The reporting in abstracts of surgical RCTs has improved after the implementation of CONSORT-A. More importantly, there is still ample room for improvement.
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 | 0.708 | 0.842 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.033 | 0.031 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".