Successful management of catastrophic peripheral vascular hemorrhage using massive autotransfusion and damage control surgery in a dog
Bibliographic record
Abstract
OBJECTIVE: To describe a case of massive transfusion using unwashed, non-anticoagulated, nonsterile autologous blood in a dog with catastrophic hemorrhage from a peripheral vessel during orthopedic surgery. A damage control surgical strategy was also employed. CASE SUMMARY: A 6-year-old, 48 kg neutered male Labrador Retriever experienced massive hemorrhage after transection of a large blood vessel while undergoing femoral head and neck osteotomy. Blood was collected from clean, but not sterile, suction canisters and clots were skimmed off. The blood was then transfused back to the dog using a standard in-line blood filter. Approximately 58% of the dog's blood volume was autotransfused in less than 2 hours, thereby meeting the criteria for massive transfusion. Surgery was aborted after hemostasis was achieved by ligation of the vessel and packing of the surgical site. Two units of fresh frozen plasma were administered postoperatively due to the development of a coagulopathy. Hemoglobinuria developed but resolved within 18 hours. Three days later, completion of the surgical procedure was performed without incident. The dog was discharged 4 days after the initial surgery. Marked swelling of the affected limb developed, but resolved after the sixth day. No other significant complications developed. NEW OR UNIQUE INFORMATION PROVIDED: In this case report, the authors describe the successful management of catastrophic hemorrhage with autotransfusion performed in the absence of sterile collection, cell washing, or anticoagulation. Although not ideal, autotransfusion under these conditions can be lifesaving in situations of massive hemorrhage. This case also highlighted the employment of a damage control surgical strategy.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".