Functional outcomes of ACL reconstruction using hamstring auto graft.
Bibliographic record
Abstract
Objectives: The knee joint is made of two cruciate ligaments. One is anterior cruciate ligament (ACL) which is weaker when it comes to comparison with the other cruciate ligament known as posterior cruciate ligament (PCL). ACL tears are most common and frequently neglected. ACL tear is affecting 70% of the population and this high incidence reflects the significance of the problem. Reason of restoration of a torn ACL is to provide knee stability, knee motion in a safe wide range and to prevent osetoarthritis OA. The gold standard for ACL auto-graft reconstruction is bone patella-tendon bone (BTB) which is still questioned by many researchers as this technique followed subjects suffered from knee pain. So aim is to use and to access outcomes of hamstring auto-graft for reconstruction ACL by using Tegner’s score. Study Design: Cross sectional study. Setting: Private based hospital, Faisalabad. Period: January 2017 to January 2019. Material and Methods: It was comprised of in comprised of 97 subjects recruited on the base of positive Tegners score. Demographic data including age was presented as mean and standard deviation. Data was stratified for the variables i.e. age, gender, duration of disease and pre-operative Tegner activity rating scale to address the effect modifiers. Post-stratification Chi-square test is applied to check the significance with P-value less than 0.05 as significant. Regression was applied to check the effect of age on Tegner’s scor. Results: This study comprised of 97 subjects (81 male, 16 females83.5.5% and 16.5% respectively from private setup based hospital with mean age of 31±11.1.Our study showed 96.9% subjects with improved Tegner’s scale after ACL reconstruction following hamstring autograph technique. Regression was also applied to check the effect of age on Tegner’s score which was found to be significant (p value0.00*). Conclusion: Hamstring technique showed good outcomes which can be used to reconstruct ACL for better quality lifestyle.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".