Disinvestment in cancer care: a survey investigating European countries’ opinions and views
Bibliographic record
Abstract
Background: The current economic context calls for rationalizing health resources that can be pursued through disinvestment from low value health technologies to invest in the best performing ones, ensuring high healthcare quality. Oncology is a field where, because of high costs of health technologies and rapid innovation, disinvestment is crucial. Methods: On this basis, the research team investigated through a survey, based on a questionnaire, opinions and views of representatives of European countries about disinvestment, in terms of fields of application, potential advocates and barriers, specifically focusing on cancer care. Results: A total of 17 questionnaires were filled in (response rate: 32.1%). The survey showed disinvestment is applied in several countries as a tool for containing health care expenditures and identifying obsolete technologies/ineffective interventions. Clinicians' resistance to change and industries' opposition are recognized as the most important barriers to the implementation of disinvestment policies. Potential targets of disinvestment in cancer are seen in diagnostic and therapeutic areas. Conclusion: Despite the agreement on fields of waste and of disinvestment policies, operational methods to put disinvestment in place are lacking. Since they should rely on an inclusive assessment of the technology, Health Technology Assessment may represent a good approach.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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".