Qualitative study of oncologists’ perceptions on the US Food and Drug Administration approval status and clinical practice guidelines and their impact on local practice patterns in India
Bibliographic record
Abstract
Objective: How the US Food and Drug Administration (FDA) approval status and international cancer care guidelines influence the practice of oncology in low-and-middle-income countries like India has not been studied so far. We aimed to study how oncologists in India perceive drug approval status and guideline recommendations for their own clinical practice. Methods and analysis: The study followed qualitative research design, incorporating semistructured interviews. The participants were qualified medical oncologists in India representing a wide range of geographical regions, including East, West, North and Southern India. Data were collected using a semistructured interview schedule. In-depth qualitative interviews were undertaken, and all interviews were transcribed verbatim. Data analysis followed the principles of thematic analysis to generate themes. Results: Of the 25 medical oncologists interviewed for this study, 15 (60%) showed awareness of the limitations of the US FDA approval, including those of accelerated approval and approvals based on phase 2 trials. They also expressed disappointment about the lack of availability and affordability of cancer drugs and wished for more representation of Indian patients in the pivotal trials leading to the US FDA approval. NCCN guidelines were the most used guidelines and participants showed strong support for local institutional guidelines. However, participants felt that resource-stratified guidelines from different societies were not very helpful. Conclusions: Oncologists in India demonstrated awareness of the limitations of the US FDA drug approvals and found resource-stratified guidelines to be unhelpful. They preferred the main guidelines and institutional protocols over resource-stratified guidelines.
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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 | 0.010 | 0.068 |
| 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 | 0.001 |
| 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".