Association of hardware removal with secondary osteonecrosis following femoral neck fractures: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: It has been controversial that whether hardware removal will increase the risk of osteonecrosis of femoral head (ONFH) in fracture-healed patients who underwent internal fixation for femoral neck fractures (FNFs). This meta-analysis aimed to clarify the association of hardware removal with secondary hardware removal-induced ONFH (HR-ONFH). METHODS: Four electronic databases (PubMed, Embase, Web of Science, Cochrane Library) were searched for eligible studies published up to March 10, 2023. Studies reporting the relative risk of hardware status (i.e., risk rate, odds ratio [OR], or hazard ratio [HR]) were included. Newcastle-Ottawa scale (NOS) was used to assess risk of bias of included observational studies. Review Manager software was used to pool ORs and adjusted ORs. RESULTS: Five studies were included into quantitative synthesis. Hardware removal was associated with a reduced risk of HR-ONFH in the synthesis of crude odds ratios (OR, 0.62, 95% CI 0.39-0.96). In the synthesis of adjusted odds ratios, hardware removal was associated with an increased risk of HR-ONFH (OR, 1.76, 95% CI 1.23-2.51). CONCLUSION: This study demonstrates that hardware removal was associated with an increased incidence of HR-ONFH in fracture-healed patients who underwent internal fixation due to FNFs.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.028 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".