Suprapatellar vs infrapatellar approaches for intramedullary nailing of distal tibial fractures: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This review was conducted to compare the efficacy of suprapatellar (SP) and infrapatellar (IP) approaches for treating distal tibial fractures with intramedullary nailing. METHOD: This systematic review included studies comparing the outcomes of patients receiving nailing for distal tibial fractures using the SP and IP approaches. We searched the Cochrane CENTRAL, MEDLINE and Embase databases for relevant studies till 18th Sep. 2022. We used the Newcastle Ottawa Scale to assess study quality and a random-effects meta-analysis to synthesize the outcomes. We used the mean difference (MD) or standardized mean difference (SMD) with the 95% confidence interval (CI) for continuous data and the odds ratio (OR) with the 95% CI for dichotomous data. RESULTS: Four studies with 586 patients (302 in the SP group and 284 in the IP group) were included in this systematic review. The SP group may have had little or no difference in pain and slightly better knee function (MD 3.90 points, 95% CI 0.83 to 5.36) and better ankle function (MD: 8.25 points, 95% CI 3.35 to 13.15) than the IP group 12 months after surgery. Furthermore, compared to the IP group, the SP group had a lower risk of malalignment (OR: 0.22, 95% CI 0.06 to 0.75; number needed to treat (NNT): 6), a lower risk for open reduction (OR: 0.58, 95% CI 0.35 to 0.97; NNT: 16) and a shorter surgical time (MD: - 15.14 min, 95% CI - 21.28 to - 9.00). CONCLUSIONS: With more advantages, the suprapatellar approach may be the preferred nailing technique over the infrapatellar approach when treating distal tibial fractures. LEVEL OF EVIDENCE: Level III, systematic review of non-randomized studies.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".