Feasibility of Golden-Hour Interventions on Improving Victim Survival Due To Road Traffic Injuries In India
Bibliographic record
Abstract
Road Traffic Injuries (RTI) is one of the most significant emerging public health challenges of the 21st century. With a global annual death toll of 1.25 million, RTI is one of the leading causes of premature death, and disproportionally affects low and middle-income countries (LMICs). India ranks top amongst LMICs in its national burden of RTI. Research into RTI prevention and trauma management is urgently needed. We undertook a scoping review of available evidence on the feasibility of “golden hour” interventions delivered to RTI victims toreduce mortality. We found limited evidence on this topic. However, most of the identified evidence highlight India’s bigger problem of gaps in pre-hospital trauma management system. Several solutions have been proposed to bridge this gap, including mobilizing community lay-persons for trauma management. The current availability of evidence is not sufficient for undertaking a systematic review. However, interventions identified in this review could form the basis for future program evaluation in their effectiveness in reducing mortality.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".