“Choosing Wisely” for Cancer Care in India
Bibliographic record
Abstract
Choosing Wisely India (CWI) is an initiative to identify low-value and/or potentially harmful practices in cancer care in India. Modeled after Choosing Wisely in the US and Canada,[1],[2],[3] the CWI project was intended to facilitate a conversation between patients, clinicians, hospitals, and policymakers on delivering high-quality, affordable cancer care. By identifying common low-value and/or harmful practices, this process aims to reduce unnecessary interventions to improve the overall quality of care, reduce patient toxicity, and reduce the financial burden on both the patient and system. The formal CWI report has been recently published in Lancet Oncology;[4] in this commentary, we provide a summary of the process, describe the Top 10 CWI list, and offer suggestions for future actions to improve the delivery of high-quality cancer in India's cancer system.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".