Successful treatment of a severe cannabinoid toxicity using extracorporeal therapy in a dog
Bibliographic record
Abstract
OBJECTIVE: To describe the use of extracorporeal therapy (ECT) to treat severe cannabinoid intoxication in a dog with severe hyperlipidemia. CASE SUMMARY: A 7-month-old female intact Labrador Retriever presented with seizures and severe hyperesthesia that were refractory to multiple anticonvulsant medications and required induction of general anesthesia with propofol and mechanical ventilation. The dog's urine yielded a strong positive signal for delta-9-tetrahydrocannabinol (THC) on urine drug test and exposure to THC oil was confirmed by the owner. Bloodwork revealed severe hyperlipidemia such that IV lipid emulsion was considered contraindicated. The dog was treated with a 3-hour ECT session, using charcoal hemoperfusion and hemodialysis in series. Neurologic signs improved during the session and mechanical ventilation was discontinued. Immediately after the session, the dog's mentation was significantly improved and seizures and hyperesthesia had ceased, although the dog remained moderately ataxic. The dog was hospitalized for 36 hours following the ECT session for continued monitoring. The dog fully recovered and was successfully discharged. NEW OR UNIQUE INFORMATION PROVIDED: To the authors' knowledge, this is the first published report to document ECT to treat THC intoxication in veterinary medicine. ECT may be considered as a treatment option for severe THC intoxication that is refractory to standard therapy or where severe hyperlipidemia precludes use of IV lipid emulsions.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".