A systematic review and meta-analysis comparing suprapatellar versus infrapatellar approach intramedullary nailing for tibal shaft fractures
Bibliographic record
Abstract
BACKGROUND: The application of the suprapatellar (SP) approach has challenged the traditional infrapatellar (IP) approach in the surgery treatment of tibial shaft fractures, yet the advantages and disadvantages still remain controversial. We included more high-quality studies for this meta-analysis and systematic review to evaluate the clinical outcomes and prognosis of both approaches and thus to provide new ideas for surgeons. METHOD: We searched literatures from PubMed, Cochrane Library, Web of Science, and EMBASE databases from January 2000 to December 2022. We extracted general information including sample size, gender, proportion of open fracture, follow-up time, and outcome indicators including entrance accuracy, fluoroscopy time, operation time, intraoperative blood loss, Lysholm score, VAS pain score, range of motion (ROM) function score, reposition accuracy, and revision cases. Cochrane Collaboration's tool and the Newcastle-Ottawa Scale were used to evaluate literature qualities. Meta-analysis was performed using RevMan 5.4 software. RESULTS: A total of 23 studies were generated that qualified for inclusion, 17 of which were used for meta-analysis. This study found statistically significant differences in coronal plane entrance accuracy, fluoroscopy time, Lysholm score, and VAS pain score. CONCLUSION: The results of our meta-analysis showed that the SP approach was significantly better than the IP approach in angle and distance entrance accuracy of coronal plane, angle entrance accuracy of sagittal plane, fluoroscopy time, Lysholm score, and VAS pain score. There were no significant differences in sagittal angle accuracy, operative time, intraoperative blood loss, and ROM score.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.047 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 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".