Clockwise Torque of Sliding Hip Screws: Is There a Right Side?
Bibliographic record
Abstract
OBJECTIVES: This study evaluated whether patients with a left-sided femoral neck fracture (FNF) treated with a sliding hip screw (SHS) had a higher implant failure rate than patients treated for a right-sided FNF. This was performed to determine the clinical relevance of the clockwise rotational torque of the femoral neck lag screw in a SHS, in relation to the rotational stability of left and right-sided FNFs after fixation. METHODS: Data were derived from the FAITH trial and Dutch Hip Fracture Audit (DHFA). Patients with a FNF, aged ≥50, treated with a SHS, with at least 3-month follow-up data available, were included. Implant failure was analyzed in a multivariable logistic regression model adjusted for age, sex, fracture displacement, prefracture living setting and functional mobility, and American Society for Anesthesiologists Class. RESULTS: One thousand seven hundred fifty patients were included, of which 944 (53.9%) had a left-sided and 806 (46.1%) a right-sided FNF. Implant failure occurred in 60 cases (3.4%), of which 31 were left-sided and 29 right-sided. No association between fracture side and implant failure was found [odds ratio (OR) for left vs. right 0.89, 95% confidence interval (CI) 0.52-1.52]. Female sex (OR 3.02, CI: 1.62-6.10), using a mobility aid (OR 2.02, CI 1.01-3.96) and a displaced fracture (OR 2.51, CI: 1.44-4.42), were associated with implant failure. CONCLUSIONS: This study could not substantiate the hypothesis that the biomechanics of the clockwise screw rotation of the SHS contributes to an increased risk of implant failure in left-sided FNFs compared with right-sided fractures. LEVEL OF EVIDENCE: Therapeutic Level II.See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".