The diagnosis and emergency care of heat related illness and sunburn in athletes: A retrospective case series.
Bibliographic record
Abstract
OBJECTIVE: Heat illnesses contribute to significant morbidity and occasional mortality in athletic populations. Sunburn increases the risk of various skin carcinomas. This report provides an overview of the etiology, symptomatology, risk identification, prevention, and treatment for heat related illnesses and sunburn. CLINICAL FEATURES: Four cases are presented to illustrate the diagnosis and immediate treatment of exercise related heat illness and sunburn. INTERVENTION AND OUTCOME: Identification of signs and symptoms combined with prompt treatment, achieved resolution in three athletes presenting with exercise related heat illness and one athlete with sunburn. CONCLUSION: The best treatment approach is prevention. Chiropractors can be an important resource for information regarding prevention and treatment strategies. For mild to moderate heat illness, quick identification of signs and symptoms, followed by rapid cooling and re-hydration comprises treatment. For heat stroke, rapid and aggressive cooling is essential to reduce mortality. Best evidence treatment of sunburn is symptomatic relief with emollients and pain control via medications.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".