Surgical limb‐sparing in veterinary medicine: A review of existing techniques in dogs
Bibliographic record
Abstract
Surgical limb-sparing in veterinary medicine can be defined as an intervention aimed at preserving limb function when a bone abnormality is present, namely neoplasia or a non-repairable fracture, and the affected segment of that bone needs to be removed and, most often, replaced. In some cases, the affected segment of bone is treated and reimplanted. It is mostly prevalent in the context of local tumor control while preserving limb function in veterinary surgical oncology but has also been employed for comminuted fracture repair. Importantly, this review focuses on neoplasia and non-repairable fractures wherein the bones were normal prior to the pathology and the non-affected segments of bone remain normal in the face of the pathology. Several techniques have been reported and vary based on a number of factors such as anatomic location of the pathology and method of addressing the defect created by removal of the affected bone segment. Limb-sparing techniques have been documented to have comparable survival times to limb amputation but can be fraught with mechanical and biological complications, requiring intensive long-term care and client compliance with treatment regimens. The most common complications are infection, mechanical failure, and local recurrence. Decreasing the risk of complication is the driving force for research in the field of limb sparing in dogs. The aim of this review was to compile the existing literature on surgical limb-sparing in dogs with the intent to guide clinical decision-making and inform further research on limb-sparing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".