[Periprosthetic femur fractures. Experience at hospital Español de México with a 2-year follow-up].
Bibliographic record
Abstract
UNLABELLED: Periprosthetic fractures have had an increasing incidence in the past decades; their resolution is a difficult challenge even for the most experienced orthopedist surgeons. The Vancouver classification has contributed to a great extent to their better characterization and therapeutic guidance. The purpose of this paper is to make a review of the most recent advances on the topic and present our experience in the treatment of these fractures. MATERIAL AND METHODS: We describe the characteristics of a sample of 20 patients with periprosthetic femur fractures treated at our hospital from March 2008 to March 2010 and typed according to the Vancouver classification. RESULTS: Mean age was 74.5 years with a range of 65-87 years. Females were predominant (70%). Five cases were type A in the classification, 10 cases type B, and 5 cases type C. Nineteen underwent surgical treatment. The mean total length of stay was 7 days, with a range of 5-12. The mean healing time for types B and C was 4 months (range 3-6 months). Four patients had complications. The resolution was assessed as excellent in 5 cases (25%), good in 11 cases (55%), and poor in 4 cases (20%). CONCLUSIONS: A thorough individual assessment based on the Vancouver classification, age and the patient's functional requirements are the major parameters for treatment success.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".