Postoperative Complications after Surgical Management of Incomplete Ossification of the Humeral Condyle in Dogs
Bibliographic record
Abstract
OBJECTIVE: To describe incidence and type of postoperative complications in the surgical management of incomplete ossification of the humeral condyle (IOHC) and identify any risk factors associated with development of these complications. STUDY DESIGN: Case series. METHODS: Clinical records of dogs (n=57) that had prophylactic transcondylar screw insertion for treatment of IOHC (79 elbows) at 6 UK referral centers were reviewed. Signalment, presentation, surgical management, postoperative care, and complications were recorded. Postoperative complications were divided into seroma, surgical site infections (SSI) and implant complications. RESULTS: Spaniel breeds and entire males were overrepresented. The overall complication rate was 59.5%. Seroma (n=25) and SSI (24) were the most commonly encountered complications. Implant failure occurred in 2 dogs. Labrador retrievers were at greater risk of developing a postoperative complication than other breeds (P=.03). Increasing bodyweight was a significant risk factor for development of a SSI (P=.03). Placement of the transcondylar screw in lag fashion rather than as a positional screw reduced the incidence of postoperative SSI (P=.007). CONCLUSIONS: Surgical management of IOHC is associated with a high rate of postoperative complications. Placement of the transcondylar screw in lag fashion may limit postoperative complications and warrants further consideration.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".