The language of prostate cancer treatments and implications for informed decision making by patients
Bibliographic record
Abstract
Previous research has shown that cancer patients lack knowledge about treatments particularly for reproductive system cancers. Focusing on prostate cancer, we explored how the language used to describe treatments and their side effects is understood by both men and women. Since the language around prostate cancer is often euphemised to reduce distress and stigma, our aim was to elucidate how language (e.g. hormone therapy vs. androgen deprivation therapy) affects both patients' and partners' attitudes towards treatment decision making. We surveyed 690 male and female cancer patients and non-patients through an online questionnaire. A large proportion of participants did not understand the terminology used to describe prostate cancer treatments. Most did not know that the terms 'chemical castration', 'hormonal therapy' and 'androgen deprivation' are synonymous. Male respondents stated that they would more readily agree to hormonal therapy than to castration to treat prostate cancer and felt significantly more strongly than women about how androgen deprivation therapy, described in various terms, affected masculinity. Men and women differed substantially in their opinion about the impact of androgen deprivation. For patients and partners to make informed decisions and cope effectively with treatment side effects, it is important that healthcare practitioners provide accurate information using language that is unambiguous.
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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 | 0.022 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".