Keep Cool but Don’t Freeze: The Influence of William J. Mills Jr. on the Treatment of Frostbite
Bibliographic record
Abstract
Dr William J. Mills Jr., an Alaskan orthopedic surgeon, helped establish the current protocols for frostbite treatment and changed a dogma used for more than 140 years that was established by Napoleon's surgeon general of the army, Baron Dominique-Jean Larrey. During Napoleon's 1812 siege of Moscow, Larrey noticed the destructive effects of using open fire heat for warming frozen body parts, so he suggested rubbing snow or immersion in cold water. Dr Mills treated many cold injuries during his medical career. After setting up his medical practice in Anchorage, Alaska, he realized the inefficiency of the established protocols and started researching new treatments for frostbite. Dr Mills followed Meryman's method of rapidly thawing frozen red blood cells in warm water. Mills and his colleagues established a treatment protocol for freezing cold injury that included rapid warming in warm water. These studies resulted in the publication of three key papers in 1960 and 1961. These papers were the first clinical studies that described rapid warming as a treatment. Subsequently, rapid warming, with some variation in water temperatures, has been accepted as the standard of treatment. Due to his outstanding contribution to the treatment of frostbite, he has been referred to as "the nation's leading authority on cold injury." Mills and his colleagues created a new classification system that divided frostbite into two levels, superficial and deep, which was more applicable in clinics than the traditional 4-tier classification. The 2-tier classification is still useful outside of the hospital setting.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".