Tilt Table Recovery of Horses After Orthopedic Surgery: Fifty‐Four Cases (1994–2005)
Bibliographic record
Abstract
OBJECTIVE: To describe an assisted anesthetic recovery technique using a tilt table for horses after high-risk orthopedic-related procedures and to evaluate outcome. STUDY DESIGN: Retrospective study. SAMPLE POPULATION: Anesthetic recoveries (n=54) for 36 horses. METHODS: Medical records (April 1994-October 2005) for horses that had high-risk orthopedic surgery and recovery from general anesthesia on a tilt table were reviewed. Information about the surgical procedure, anesthetic and recovery period was analyzed. RESULTS: Of 54 anesthetic recoveries, 1 horse (1.9%) had complete failure of internal fixation during recovery and was euthanatized. Six (11% recoveries) horses failed to adapt to the tilt table system, which necessitated transfer to a conventional recovery room. Complications without important consequences for clinical outcome (skin abrasions, myositis, cast breakage, partial implant failure) occurred during 8 (15%) recoveries. CONCLUSIONS: A tilt table recovery system was associated with minimal incidence of serious complications. Potential disadvantages of the system are the number of personnel required, longer recovery time, and the need for a specialized table. CLINICAL RELEVANCE: A tilt table is a useful system for recovering horses believed to be at increased risk of injury during anesthetic recovery after high-risk orthopedic-related procedures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".