Hyperkalemia during general anesthesia in two Greyhounds
Bibliographic record
Abstract
CASE DESCRIPTION: A 36-kg (79-lb) castrated male Greyhound (dog 1) and a 25-kg (55 lb) spayed female Greyhound (dog 2) underwent general anesthesia for dental care with similar perianesthetic protocols on multiple occasions from 2013 to 2016. Both dogs had periodontal disease but were otherwise deemed healthy. Both dogs developed clinically relevant hyperkalemia, with signs including loss of P waves on ECG tracings, during multiple anesthetic events. CLINICAL FINDINGS: Dog 1 developed hyperkalemia during 2 of 2 anesthetic events, with ECG changes noted during the first event. Dog 2 developed hyperkalemia during 3 of 4 anesthetic events, with ECG changes identified during the second and third events. Serum potassium concentration for both dogs was within the reference range prior to and between anesthetic events. No underlying etiopathogenesis for hyperkalemia was identified for either dog. TREATMENT AND OUTCOME: In each hyperkalemic event, the clinician stopped the dental procedure and continued to provide supportive care and monitoring while the dog recovered from anesthesia. The ECG changes resolved, and serum potassium concentration returned to the reference range rapidly after inhalant anesthetic administration was discontinued. The dogs were discharged from the hospital without further complications. CLINICAL RELEVANCE: Hyperkalemia in anesthetized Greyhounds resulted in serious cardiac conduction abnormalities, which could be potentially fatal if not recognized and promptly treated. Further investigation into the etiopathogenesis, prevention and treatment strategies, and genetic or familial components of this condition is indicated.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".