Helping patients with localized prostate cancer reach treatment decisions.
Bibliographic record
Abstract
OBJECTIVE: To highlight the role of psychosocial variables in treatment decision making for patients with localized prostate cancer and how family physicians can be of most help to such patients in facilitating good treatment choices. QUALITY OF EVIDENCE: PubMed was searched, and articles relevant to the psychosocial aspects of localized prostate cancer treatment decision making were included. Articles were excluded when they clearly specified inclusion of men with metastatic disease. This is not a systematic review, and recommendations made are drawn from studies of level II or III evidence. MAIN MESSAGE: The optimal strategy for managing localized prostate cancer has not been established and currently includes a number of potential options: active surveillance, radical prostatectomy, external beam radiotherapy, brachytherapy, and cryoablation. Consequently, men often struggle during the decision-making process, and some later regret their decisions. With an increased awareness of the psychosocial aspects of patient decision making, family physicians can help patients make better decisions. CONCLUSION: Family physicians can help minimize the decisional regret experienced by patients after treatment by encouraging patients to consider their values and social supports, as well as the accuracy and appropriateness of the information used in the decision-making process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".