Attitude of Iranian Medical Oncologists Toward Economic Aspects, and Policy-making in Relation to New Cancer Drugs
Bibliographic record
Abstract
BACKGROUND: Although medical oncologists can have an important role in controlling the cost of cancer treatment, there is little information about their attitudes toward the cost of cancer treatment and the impact of cost on their treatment recommendations, especially in low- and middle-income countries (LMICs). In this study, we assessed the attitude of Iranian medical oncologists toward some economic aspects of new cancer drugs. METHODS: We translated a questionnaire that was used in similar studies in the United States and Canada into Persian and modified it according to the local setting in Iran. The face and content validity of the questionnaire were assessed by oncologists before being used in the survey. We distributed the questionnaire and collected the data from 80 oncologists who participated in the 13th Annual Congress of the Iranian Society of Medical Oncology and Hematology (ISMOH). RESULTS: Fifty-two oncologists participated in our study (a response rate of 65%). The majority of oncologists stated that drug costs and patient out-of-pocket (OOP) costs influence their treatment recommendations (92% and 94%, respectively). Most oncologists (70%) felt that they are ready enough to use cost-effectiveness information in their treatment decisions, and 74% believed that patients should only have access to cancer treatments that are cost-effective. Most oncologists agree that the government should have control over drug prices, and more use of cost-effectiveness data is required for decision-making about cancer drug coverage. Ninety-one percent of oncologists said that they always or frequently discuss cancer treatment costs with their patients. Oncologists believed that academic groups (research centers and scientific societies) (81%) and the Ministry of Health (MoH) (43%) are the most eligible groups for determining whether a drug provides good value. CONCLUSION: Iranian medical oncologists are ready to participate in the health technology assessment and priority-setting process. This situation creates a unique opportunity for the government to rely on scientific societies and find an appropriate solution for the improvement of patients' access to high-quality care.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".