Case report: Manual carbon hemoperfusion for the treatment of meloxicam toxicity in a cat and suspected ibuprofen toxicity in a dog
Bibliographic record
Abstract
Extracorporeal blood purification (ECBP) has become a popular treatment option for non-steroidal anti-inflammatory drug (NSAID) toxicity in small animals. However, challenges arise when using ECBP for small dogs and cats because the priming volume required by most machine-based ECBP platforms might be excessive, leading to cardiovascular instability if a blood prime is not used. This report describes the successful use of manual carbon hemoperfusion (MCHP) to reduce plasma meloxicam levels in a cat following an inadvertent overdose and its use in a dog following suspected ibuprofen ingestion. In both animals, MCHP reduced the circuit volume needed for ECBP from 125 mL with a machine-based therapeutic plasma exchange or 104 mL with an in-series carbon hemoperfusion on an intermittent hemodialysis platform to just 40-50 mL. In the cat, MCHP reduced plasma meloxicam levels by 44%, and in both animals, the use of MCHP in these pets was well-tolerated and safe. Due to pre-existing anemia, the cat required a blood transfusion but the dog did not. MCHP is technically simple and can be performed at any hospital with access to carbon filters and blood bank resources. This technique may represent a reasonable alternative to treat NSAID toxicities in animals that are too small for conventional extracorporeal decontamination methods using either machine-based platforms without using a blood prime or in locations where these machines are unavailable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".