Predictors of failure for cephalomedullary nailing of proximal femoral fractures
Bibliographic record
Abstract
The purpose of this study was to identify factors that predict implant cut-out after cephalomedullary nailing of intertrochanteric and subtrochanteric hip fractures, and to test the significance of calcar referenced tip-apex distance (CalTAD) as a predictor for cut-out. We retrospectively reviewed 170 consecutive fractures that had undergone cephalomedullary nailing. Of these, 77 met the inclusion criteria of a non-pathological fracture with a minimum of 80 days radiological follow-up (mean 408 days; 81 days to 4.9 years). The overall cut-out rate was 13% (10/77). The significant parameters in the univariate analysis were tip-apex distance (TAD) (p < 0.001), CalTAD (p = 0.001), cervical angle difference (p = 0.004), and lag screw placement in the anteroposterior (AP) view (Parker's ratio index) (p = 0.003). Non-significant parameters were age (p = 0.325), gender (p = 1.000), fracture side (p = 0.507), fracture type (AO classification) (p = 0.381), Singh Osteoporosis Index (p = 0.575), lag screw placement in the lateral view (p = 0.123), and reduction quality (modified Baumgaertner's method) (p = 0.575). In the multivariate analysis, CalTAD was the only significant measurement (p = 0.001). CalTAD had almost perfect inter-observer reliability (interclass correlation coefficient (ICC) 0.901). Our data provide the first reported clinical evidence that CalTAD is a predictor of cut-out. The finding of CalTAD as the only significant parameter in the multivariate analysis, along with the univariate significance of Parker's ratio index in the AP view, suggest that inferior placement of the lag screw is preferable to reduce the rate of cut-out.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".