A Systematic Review of the Management of Knee Osteoarthritis by Proximal Fibular Osteotomy in the Indian Population
Bibliographic record
Abstract
This systematic review aims to assess the management of knee osteoarthritis through proximal fibular osteotomy (PFO) in the Indian population by synthesizing data from various prospective cohort and interventional studies. We seek to provide an overview of the effectiveness and safety of PFO as a treatment modality and offer insights into its potential implications for clinical practice in India. A systematic search strategy was employed, targeting multiple medical databases to identify relevant studies published from 2018 to 2023. Inclusion criteria encompassed studies involving Indian patients with medial compartment knee osteoarthritis and varus deformity who underwent PFO. Data were extracted and evaluated according to the Newcastle-Ottawa Scale for observational studies. Eight studies were included in this review, each displaying varying designs, patient populations, and follow-up duration. The findings consistently indicated that PFO improved pain, knee function, and radiological outcomes, such as knee joint space and tibio-femoral angles. These improvements were generally sustained over several months to a year. The available evidence underscores the potential of PFO as a promising intervention for managing knee osteoarthritis in the Indian population, particularly in patients with medial compartment involvement and varus deformity. While these results are promising, the limitations inherent in the current literature, including study design variations and small sample sizes, necessitate further research with more extensive and diverse patient populations. This systematic review provides valuable insights for healthcare professionals and researchers, highlighting the need for more rigorous investigations and supporting the consideration of PFO as a viable treatment option for knee osteoarthritis in India.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".