Surgical versus nonsurgical treatment of primary acute patellar dislocation
Bibliographic record
Abstract
BACKGROUND: To systematically review the efficacy of surgical versus nonsurgical treatment for acute patellar dislocation. MATERIALS AND METHODS: PubMed, Cochrane, and Embase were searched up to February 12, 2019. After removing duplicates, preliminary screening, and reading the full texts, we finally selected 16 articles, including 11 randomized controlled trials and 5 cohort studies. The quality of the enrolled studies was evaluated by Jadad score or Newcastle-Ottawa scale. Meta-analyses were performed using odds ratio (OR) and standardized mean difference (SMD) as effect variables. The clinical parameters assessed included mean Kujala score, rate of redislocation, incidence of patellar subluxation, patient satisfaction, and visual analog scale (VAS) for pain. Evidence levels were determined using GRADE profile. RESULTS: The 16 included studies involved 918 cases, 418 in the surgical group and 500 in the nonsurgical group. The results of the meta-analysis showed higher mean Kujala score (SMD = 0.79, 95% confidence interval [CI] [0.3, 1.28], P = .002) and lower rate of redislocation (OR = 0.44, 95% CI [0.3, 0.63], P < .00001) in the surgical group than the nonsurgical group, but showed insignificant differences in the incidence of patellar subluxation (OR = 0.61, 95% CI [0.36, 1.03], P = .06), satisfaction of patients (OR = 1.44, 95% CI [0.64, 3.25], P = .38), and VAS (SMD = 0.84, 95% CI [-0.36, 9.03], P = .84). CONCLUSION: For patients with primary acute patellar dislocation, surgical treatment produces a higher mean Kujala score and a lower rate of redislocation than nonsurgical treatment.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".