A gender-bias-mitigated, data-driven precision medicine system to assist in the selection of biological treatments of grade 3 and 4 knee osteoarthritis: development and preliminary validation of precisionKNEE
Bibliographic record
Abstract
Introduction Osteoarthritis is a leading cause of global disability and is set to worsen with the concurrent rise in rates of obesity and an ageing population [1]. Current clinical solutions are sub-optimal with regards to their invasiveness and outcomes. Orthopaedic biologics is an emerging field that offers alternative and parallel treatment options to address this problem. Determining which patients will benefit most from these novel treatments is key in developing clinical pathways. Methods Our dataset included 329 patients treated with microfragmented fat injection (MFAT) over a 2 year period. Clinico-demographic data was recorded as well as 1-year Oxford Knee Score (OKS). The data was modelled to predict OKS 1-year response using Random Forest Regressors. Gender-bias was mitigated and outliers were hidden from the training model. The model was validated on raw test data and on a subset of patients with Kellgren-Lawrence grade 3 and 4 radiological evidence of arthritis, age greater than 64, preoperative OKS less than or equal to 27 and idiopathic aetiology of arthritis. Results The mean age and mean body mass index (BMI) of patients in our dataset was 66.4 years, 26.9 respectively. 53.5% of patients had Kellgren-Lawrence grade 4 arthritis. The final models RMSE was 6.72, MAE was 5.38 and r-squared was 0.23 on raw test data. An RMSE of 9.77 and MAE of 7.81 was achieved when validating the model on our subset of patients. Wilcoxon signed rank tests found no evidence of predicted results being statistically significantly different to ground truth values (p ¿ 0.05). Preoperative OKS and Kellgren-Lawrence arthritis grade was the most important feature in our model. Discussion Our model is performant and able to predict 1 year OKS response outcome within our set of patients. We have found key features of prediction and would recommend these are researched further to improve model performance. Our dataset does not compare outcomes with other standard treatments. We also don’t compare outcomes with other biologic treatments. Ultimately, this research can be used as a tool to benefit both patients and clinicians in a combined decision-making process.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".