537 Burn Clinical Trials: A Systematic Review of Registration and Publications
Bibliographic record
Abstract
Randomized controlled clinical trials (CTs) are gold standard tools for assessing interventions. Although burn CTs have improved care, their status, publication frequency, and publication quality are not known. Here, we sought to characterize burn CTs by analyzing their location, completion status, temporal trend, funding sources, and the quality of trial reporting. The study background, rationale, search strategy, inclusion/exclusion criteria, and data analysis were published in PROSPERO register ahead of the data analysis. CT records were obtained from ClinicalTrials.gov and WHO’s CT Registry (searched May 2017). Publications were obtained from PubMed, Google Scholar, OVID MEDLINE, and ClinicalTrials.gov (searched June 2017). 23-item rubric adapted from CONSORT and ICH E3 guidelines. 738 burn CTs were identified globally, of which majority were publically-funded (77%), ongoing (52%), and assessed behavioral, pharmacological, device-based, dietary-based, and biological/procedural interventions. Amongst the ended trials, 69 (28%) published their findings. Significantly fewer industry-funded trials published findings (14% vs 33% publically-funded). Quality of reporting was suboptimal, and most underreported categories were the trial phase, severity, and sample size estimation. Burn trials are proliferating in number, location, and interventions assessed. Only a small proportion are published and quality of reporting is suboptimal. Incomplete, outdated, and non-registered CTs which are difficult to track. Burn researchers should aim to register and report on all clinical trials regardless of the outcome. Superior a priori design can reduce precocious termination and mandatory reporting of data fields can improve quality of reporting. Systematic review registration number: CRD42017068549
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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 | 0.090 | 0.276 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.043 | 0.048 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".