Functional outcomes and quality of life after surgically treated tibial plateau fractures
Bibliographic record
Abstract
BACKGROUND: Tibial plateau fractures (TPF) are uncommon and challenging for orthopedic surgeons with controversial reported outcomes. In this study, we aimed to evaluate the functional outcomes and quality of life (QOL) of patients with surgically treated TPF. METHODS: A total of 80 consecutive patients and 82 controls participated in this case control study. The patients were all surgically treated in our tertiary center from April 2012 to April 2020. The functional outcome was evaluated using the Western Ontario and McMaster Universities Arthritis Index (WOMAC) scale. Moreover, we used the Short Form 36 health survey (SF-36) health survey to evaluate the QOL. RESULTS: No significant difference was observed in the overall mean SF-36 score in the two groups. We found a significant positive correlation between the scores of the SF-36 and WOMAC questionnaires (r = 0.642, p < 0.001) and between the ROM and the WOMAC questionnaire score (r = 0.478, p < 0.001). Further, ROM and SF-36 showed a weak positive correlation (r = 0.248, p = 0.026). Age had a weak negative correlation with the pain subscale of SF-36 (r = - 0.255, p = 0.22), even though it was not correlated with the total score or other subscales (p > 0.05). CONCLUSION: QoL after TPF is not significantly different from that of a matched control group. Also, neither age nor BMI correlates with the QoL and functional outcome.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".