Quality of reporting in hyperbaric medicine clinical trials: a cross-sectional study
Bibliographic record
Abstract
Introduction: Research in hyperbaric oxygen (HBO) medicine is growing, but the quality of HBO studies is variable. Low study quality may compromise evidence-based decision-making and clinical translation. Methods: This cross-sectional study examined the adherence of 50 randomly selected HBO clinical trials (25 randomised controlled trials [RCTs] and 25 observational studies) to relevant core reporting guidelines: consolidated standards of reporting trials (CONSORT), non-pharmacologic treatments (NPT), and strengthening the reporting of observational studies in epidemiology (STROBE). Studies published in peer-reviewed journals between January 2018 and May 2023 and indexed on PubMed were analysed. Reporting quality was classified as 'excellent' (> 85% of guideline items adequately reported), 'good' (50-85%), or 'poor' (< 50%). Results: The sample represented 29% of RCTs and 16% of observational studies for the timeframe assessed. No study was rated as 'excellent' for completeness, 28 (56%) were rated as 'good', and 22 (44%) as 'poor'. In RCTs, only one study (4%) adequately reported protocol adherence and eight studies (32%) reported blinding procedures. The NPT checklist showed that key items, including care provider adherence (0 studies) and participant adherence (one study; 4%), were frequently not reported. For observational studies, basic design elements were adequately reported, but with significant gaps in bias management (nine studies; 36%) and missing data handling (13 studies; 52%). Only six studies (12%) mentioned the use of reporting guidelines. Conclusions: Our results showed that quality of reporting of HBO studies is suboptimal. These findings highlight the need for increased awareness and implementation of reporting guidelines, as well as the potential development of HBO-specific guidelines.
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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 | 0.553 | 0.703 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".