Reducing Health Inequities Through Total Knee Arthroplasty: An Experience From Bhutan
Bibliographic record
Abstract
Background and Aims: Bhutan is a low-middle-income country with a 0.7 million population with a high burden of musculoskeletal conditions. Recognizing the high burden of osteoarthritis, total knee arthroplasty (TKA) was launched in the country in 2022. However, Bhutan continues to refer complicated cases to India. In 2024, International Operation, a US-based nonprofit secular and humanitarian organization, conducted a TKA camp in Bhutan. This perspective aims to report about the camp and discuss how such camps help reduce healthcare disparities. Method: We compiled data on patients who underwent total knee or hip arthroplasty in last 7 years from the registry maintained at National Referral Hospital of Bhutan. We shared our experience of hosting TKA camp and discuss how such camps might help reduce healthcare disparities. Result: In last 7 years, Bhutan referred increasing number of patients for total knee and hip arthroplasty to India. Royal Government of Bhutan spends Nu. 250,000 (approximately US$3000) per patient excluding expenses for travel, logistics, and medications. A team from International Operation conducted TKA on 31 patients during the camp. Conclusion: Such camps would help reduce the healthcare disparities in low- and middle-income countries.
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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 | 0.002 | 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 | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".