Bibliographic record
Abstract
High altitude illness (HAI) is the current accepted clinical term for a group of disorders including acute mountain sickness (AMS), high-altitude cerebral edema (HACE), and high-altitude pulmonary edema (HAPE), which occur in travelers visiting high-altitude locations. High-altitude illness is due to hypobaric hypoxia, is not associated with age or physical conditioning, and mild forms are easily treated. High-altitude cerebral edema and HAPE are medical emergencies that are fatal if not promptly treated and fortunately are uncommon. The cause of most high-altitude fatalities is not related to HAI and can be easily distinguished from HACE and HAPE; however, other causes of death may have symptoms and physical findings that overlap with HAI, making postmortem diagnosis challenging. Fatalities due to HAPE and HACE are diagnoses of exclusion. Medical examiners and coroners who work in jurisdictions with high-altitude locations should be aware of the risk factors, physiology, pathology, differential diagnosis, and classification of HAI to accurately recognize HAI as a cause of death. Medical examiners who do not work in jurisdictions with high-altitude locations may be asked to evaluate deaths that occur overseas associated with high-altitude trekking and mountaineering activities.
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 | 0.000 |
| 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 | 0.002 |
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".