Bibliographic record
Abstract
Femoral fractures occur commonly in dogs and cats, accounting for 45% of all long bone fractures. Femoral fractures are classified based on anatomic locational and include fractures of the proximal epiphysis, proximal physeal fractures, subcapital fractures, fractures of the femoral neck, trochanteric fractures, subtrochanteric fractures, fractures of the femoral shaft, supracondylar fractures, distal physeal fractures, unicondylar fractures, bicondylar fractures and fractures affecting the femoral trochlea. In general, femoral fractures are not amenable to treatment with external coaptation, so surgical stabilisation or a salvage procedure is required. Selection of an implant system will depend on fracture configuration and location, and requires a thorough understanding of the forces to which the implant system will be subjected. Complications associated with stabilisation may include premature physeal closure, resorption of the femoral head or neck, malunion, non-union, altered coxofemoral development, implant failure, sciatic neurapraxia, quadriceps contracture, patellar luxation and infection. The complication rate can be substantially reduced by the use of meticulous surgical technique and appropriate implant selection with the prognosis for complete functional recovery remaining good to excellent, providing that an optimal healing environment is preserved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.063 | 0.010 |
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".