The use of postoperative bone scintigraphy to predict graft retention.
Bibliographic record
Abstract
BACKGROUND: Osteonecrosis of the femoral head commonly occurs in patients aged under 50 years. Because of a high rate of complications associated with joint replacement surgery in this population, surgical techniques, such as vascularized fibular grafting, have been devised in an attempt to salvage the femoral head. The purpose of this study was to investigate the use of bone scintigraphy to predict graft retention after vascularized fibular grafting for osteonecrosis of the hip. METHODS: We evaluated single photon emission computed tomography images from 104 subjects whose hips were treated with vascularized fibular grafts between 1994 and 2000. We compared the signal intensity of the graft with the intensity of the ipsilateral proximal femoral diaphysis and assigned a score of 1 if less than diaphysis, 2 if equal to diaphysis and 3 if greater than diaphysis. We defined graft failure as conversion to or on the waiting list for total hip arthroplasty. RESULTS: Thirty percent of hips failed treatment (n = 31, mean graft survival 34.9 mo), while 70% of grafts were retained (n = 73, mean follow-up 56.6 mo). Bone scan scores were significantly lower in the failed group (mean 7.1, range 6-12), compared with the retained group (mean 8.5, range 6-18; p = 0.03). Logistic regression demonstrated that a bone scan score > 6 was associated with graft retention (p = 0.028), with an odds ratio of 3.08 (range 1.13-8.40). CONCLUSION: These results suggest that having a well-perfused graft in the early postoperative period improves the chances of graft retention in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".