The use of high‐dose insulin therapy and intravenous lipid emulsion to treat severe, refractory diltiazem toxicosis in a dog
Bibliographic record
Abstract
OBJECTIVE: To describe the novel use of high-dose insulin (HDI) therapy and intravenous lipid emulsion (ILE) to treat refractory, severe diltiazem toxicosis in a dog. CASE SUMMARY: A 4-year-old Pomeranian was presented for treatment 2.5 hours following ingestion of a diltiazem extended-release capsule. Toxic ingestion was calculated at a maximum exposure of 79 mg/kg, with a reported canine LD50 of 50 mg/kg. Clinical signs of progressive hypotension and severe bradycardia with atrial standstill were observed, which persisted despite treatment with atropine, calcium, glucagon, and dopamine. The novel use of HDI and ILE as part of therapy for diltiazem toxicosis resulted in clinical resolution of life-threatening signs. Within 1 hour of initiating HDI therapy, the clinical signs improved, and with continued treatment, the patient remained normotensive and survived to discharge. NEW OR UNIQUE INFORMATION PROVIDED: To the authors' knowledge, this is the first reported clinical case describing the use of both HDI and ILE therapy in the treatment of severe refractory diltiazem toxicosis in veterinary medicine. No significant adverse effects were observed from the treatment. In veterinary patients with severe refractory calcium channel blocker toxicosis, the use of HDI and ILE should be considered for life-threatening clinical signs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".