Clinicians’ and Patients’ Perceptions and Use of the Word “Cured” in Cancer Care: An Italian Survey
Bibliographic record
Abstract
BACKGROUND: The words "hope" and "cure" were used in a greater number of articles and sentences in narrative and editorial papers than in primary research. Despite concomitant improvements in cancer outcomes, the related reluctance to use these terms in more scientifically oriented original reports may reflect a bias worthy of future exploration. This study aims to survey a group of physicians and cancer patients regarding their perception and use of the word cure. MATERIALS AND METHOD: An anonymous online and print survey was conducted to explore Italian clinicians' (the sample includes medical oncologists, radiotherapists, and oncological surgeons) and cancer patients' approach to the perception and use of the word "cure" in cancer care. The participants received an email informing them of the study's purpose and were invited to participate in the survey via a linked form. A portion, two-thirds, of questionnaires were also administered to patients in the traditional paper form. RESULTS: The survey was completed by 224 clinicians (54 oncologists, 78 radiotherapists, and 92 cancer surgeons) and 249 patients. The results indicate a favourable attitude for patients in favour of a new language ("cured" vs. "complete remission") of the disease experience. CONCLUSIONS: The use of the word cured is substantially accepted and equally shared by doctors and patients. Its use can facilitate the elimination of metaphoric implications and toxic cancer-related connotations registered in all cultures that discourage patients from viewing cancer as a disease with varied outcomes, including cure.
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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 | 0.006 | 0.019 |
| 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 | 0.002 | 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".