Reducing Distortion – Identifying Areas to Improve the Quality of Randomized Clinical Trials Published in Anesthesiology Journals
Bibliographic record
Abstract
Randomized clinical trials (RCTs) provide important evidence to inform clinical decision making; if these trials are of low quality, the resulting clinical decision will likely also be of low quality. The main purpose of this thesis was to conduct a series of methodological surveys that would identify potential areas of improvement in the quality of reporting for RCTs published in anesthesiology journals. Trial registration adequacy, adherence to CONSORT for Abstracts guidelines, and sample size calculation quality were all assessed, with a final chapter exploring the effect of industry funding on these methodological quality measures. While the results suggest improvement over time, the overall quality is still lacking. Industry sources funded a minority of the included RCTs, and did not appear to affect any of the measures of quality. More research is needed to confirm these findings and to identify tools for reducing the potential distortion emanating from low quality design and reporting.
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 | 0.800 | 0.930 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".