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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".