From the prehospital literature
Bibliographic record
Abstract
edited by Malcolm Wollard ▴ Richard J, Osmond MH, Nesbitt L, et al . Management and outcomes of pediatric patients transported by emergency medical services in a Canadian prehospital system. Can J Emerg Med2006;8:6–12. [OpenUrl][1] This Canadian prospective cohort study explored the types of interventions given to children by pre-hospital emergency medical services (EMS). It recruited a contiguous group of 1377 children under 16 years of age (mean 8.2 years) attended by EMS during a 6 month period. The most common presenting conditions were trauma (44.9%), seizure (11.8%) and respiratory distress (8.8%). The study showed that, despite EMS providers having a major role in treating these conditions, lifesaving interventions, particularly airway management skills, were rarely used. These included intravenous drug administration (1.4%), bag valve mask ventilation (0.3%) and endotracheal intubation (0.1%). This may be explained by the high rate of patients (28%) not transported and a low rate of urgent transports (7%) for admission to hospital. However, it demonstrates that EMS providers have very little opportunity to maintain their paediatric skills. The study found most pre-hospital practitioners would not have the opportunity to ventilate a single child in one year. This has important implications for pre-hospital education. While … [1]: {openurl}?query=rft.jtitle%253DCan%2BJ%2BEmerg%2BMed%26rft.volume%253D8%26rft.spage%253D6%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.174 | 0.074 |
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".