Bibliographic record
Abstract
PURPOSE OF REVIEW: The prehospital care of injured patients has been surrounded with much controversy over the years. This controversy exists regarding the specific interventions used on-scene and en-route to definitive care centers, regarding the overall approach to the care of these patients (advanced life support versus basic life support) and with regards to who should be providing this care. This section of the journal aims to review the most current literature concerning these topics as well as highlight some important and relevant literature preceding it. RECENT FINDINGS: Studies examining overall prehospital care in terms of morbidity, mortality and cost have been published over the last year and important points from these studies are highlighted in the text. Unfortunately, there have not been any recent, appropriately powered, prospective studies that help in clarifying this controversy. An international study of prehospital care has recently been completed and is summarized. Recent studies looking at specific aspects of prehospital care (endotracheal intubation, intravenous access and therapy, rural trauma) are also outlined in the text. SUMMARY: There is no convincing evidence that prehospital advanced life support in the urban setting provides any benefit to injured patients in terms of either morbidity or 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 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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".