Suprapatellar versus infrapatellar approach for tibia intramedullary nailing: A meta-analysis
Bibliographic record
Abstract
OBJECTIVE: This meta-analysis was performed to determine the efficacy of suprapatellar versus infrapatellar approach for tibia intramedullary nailing (IMN). METHODS: A systematic search was performed in PubMed, Embase, Cochrane library, CNKI and Wanfang. Cochrane collaboration's tool and the Newcastle-Ottawa scale were used to evaluate literature qualities. Meta-analysis was performed using RevMan 5.3 software. RESULTS: Eight studies were eligible, including two randomized controlled trials (RCTs) and six retrospective cohort trials. There were no significant differences between suprapatellar and infrapatellar approaches in operation time, coronal plane alignment, and incidence of postoperative deep infection, nonunion and secondary operation. However, suprapatellar nailing achieved a significant shorter fluoroscopy time, less VAS pain score, better sagittal plane alignment and lower incidence of angular malalignment. Though pooled results indicated no significant difference in terms of final follow-up knee functional score, the RCT subgroup analysis showed that a higher knee functional score existed in suprapatellar group. CONCLUSIONS: For tibia IMN, suprapatellar approach might be superior to infrapatellar approach with shorter fluoroscopy time, less knee pain, better knee function recovery, and more accurate fracture reduction. Meanwhile, no increased risk of postoperative complications was identified. More RCTs are required for further research.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.036 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".