Eye Problems in Mountain and Remote Areas: Prevention and Onsite Treatment—Official Recommendations of the International Commission for Mountain Emergency Medicine ICAR MEDCOM
Bibliographic record
Abstract
Although eyes are not frequently injured in the mountains, they are exposed to many adverse factors from the environment. This article, intended for first responders, paramedics, physicians, and mountaineers, is the consensus opinion of the International Commission for Mountain Emergency Medicine (ICAR-MEDCOM). Its aim is to give practical advice on the management of eye problems in mountainous and remote areas. Snow blindness and minor injuries, such as conjunctival and corneal foreign bodies, could immobilize a person and put him or her at risk of other injuries. Blunt or penetrating trauma can result in the loss of sight in the eye; this may be preventable if the injury is managed properly. In almost all cases of severe eye trauma, protecting the eye and arranging an immediate evacuation are necessary. The most common eye problems, however, are due to ultraviolet light and high altitude. People wearing contact lenses and with previous history of eye diseases are more vulnerable. Any sight-threatening eye problem or unexplained visual loss at high altitude necessitates descent. Wearing appropriate eye protection, such as sunglasses with sidepieces and goggles with polarized or photochromic lenses, could prevent most of the common eye problems in mountaineering.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".