Bibliographic record
Abstract
Kali, a two year old spayed female Labrador Retriever, was referred to the Emergency Service at the Cornell University Hospital for Animals (CUHA) for suspected heat stroke twenty-four hours earlier now complicated by vomiting, diarrhea, and disseminated intravascular coagulation (DIC). At the CUHA, Kali was normothermic, yet tachypneic, tachycardic, and dehydrated with petechiae on her ventral abdomen and blood in her feces. Routine hemogram, serum biochemical panel, urinalysis, and coagulation profile confirmed renal failure and DIC as well as several other abnormalities consistent with heat stroke. Kali was hospitalized and treated with intravenous fluids, fresh frozen plasma, gastroprotectants, broad spectrum antibiotics and antiemetics. For the next five days, Kali was managed by the Small Animal Internal Medicine Service for complications associated with heat stroke, but was subsequently discharged to the care of her owners. This paper will cover the recognition and diagnosis of heat stroke, its treatment in the acute stage, and the management of the numerous complications that can arise secondary to hyperthermia.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".