Bibliographic record
Abstract
BACKGROUND: Prehospital analgesia options for paramedics have been limited due to the difficulty in achieving safe and effective pain relief without compromising transportation to hospital. The present paper identifies the analgesia methods currently available in the prehospital setting so as to evaluate the various options and highlight areas for future research. METHODS: A literature review of Medline and Embase databases from 1966 until the present was undertaken. Further hand searching of all the references identified in these papers was also performed. All current literature was analysed and categorized according to one of four levels of evidence using National Health and Medical Research Council of Australia guidelines (1999). RESULTS: There is a paucity of randomized control trials relating to prehospital analgesia. All published literature was level III or IV prospective or retrospective studies. Drug options used included nitrous oxide/oxygen mixtures, intravenous/intramuscular nalbuphine, intravenous tramadol and intravenous pure opiate agonists. CONCLUSIONS: The evidence supporting analgesic options in the prehospital setting is limited. There are few published data in this area despite the inadequacy of pain relief being recognized as a weakness in prehospital care. Prehospital analgesia is an area worthy of innovative methods for the administration of safe and effective analgesics without significant impact on transport times. Such methods should be prospectively evaluated in well-constructed trials.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".