The Number, Content, and Quality of Randomized Controlled Trials in the Prevention and Care of Injuries
Bibliographic record
Abstract
BACKGROUND: Injuries represent an important and growing global burden of disease. The availability of evidence-based injury control interventions is unknown. We aimed to assess trends with respect to the number, content, and methodologic quality of reports of randomized controlled trials (RCTs) in the prevention and care of injuries. METHODS: We searched MEDLINE and the Cochrane Central Register of Controlled Trials for reports of RCTs of interventions in the prevention and care of injuries published between January 1, 1966 and January 1, 2006. Ten percent of reports, stratified by year, were randomly sampled. Studies were abstracted using a standardized form for nature of intervention, sample size, patients studied, and methodologic quality. RESULTS: The rate of publication of injury-related RCTs increased from 1.2 to 5.3 articles per 100 RCTs published in MEDLINE (p < 0.001) during the study period. A total of 308 reports were included in the analysis of content and methodologic quality. Poisonings (31 [10%]) were the single most common isolated mechanism of patient injury studied, whereas only 12 reports (4%) focused on road traffic injuries. The majority of interventions were hospital based (211 [68%]) and half (45%) were judged to be only available in high-income countries. Allocation concealment was deemed adequate in 73 articles (24%). One quarter of reports documented blinding of participants (77 [25%]), investigators (68 [22%]), and outcome assessors (83 [27%]). Only 44 articles (14%) reported intention-to-treat analyses. CONCLUSIONS: The number of RCTs specific to the prevention and care of injuries is small, but increasing. The reporting of injury-related RCTs has important deficiencies including inadequate allocation concealment; failure to blind patients, investigators, and assessors; and per protocol analyses. In addition, many interventions studied are not available in low-income countries where the majority of the world's population resides and injury rates are highest.
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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 | 0.650 | 0.910 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.018 |
| Bibliometrics | 0.036 | 0.029 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.011 | 0.006 |
| 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".