Managing Moderate and Severe Pain in Mountain Rescue
Bibliographic record
Abstract
AIMS: We aimed to describe evidence-based options for prehospital analgesia, and to offer practical advice to physicians and nonphysicians working in mountain rescue. METHODS: A literature search was performed; the results and recommendations were discussed among the authors. Four authors considered a scenario. The final article was discussed and approved by the International Commission for Mountain Emergency Medicine (ICAR MEDCOM) in October 2013. RESULTS AND RECOMMENDATIONS: Many health care providers fail to recognize, assess, and treat pain adequately. Assessment scales and treatment protocols should be implemented in mountain rescue services to encourage better management of pain. Specific training in assessing and managing pain is essential for all mountain rescuers. Persons administrating analgesics should receive appropriate detailed training. There is no ideal analgesic that will accomplish all that is expected in every situation. A range of drugs and delivery methods will be needed. Thus, an 'analgesic module' reflecting its users and the environment should be developed. The number of drugs carried should be reduced to a minimum by careful selection and, where possible, utilizing drugs with multiple delivery options. A strong opioid is recommended as the core drug for managing moderate or severe pain; a multimodal approach may provide additional benefits.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".