Prostate Cancer Patient Perspectives on the Use of Information in Treatment Decision-Making: A Systematic Review and Qualitative Meta-synthesis.
Bibliographic record
Abstract
BACKGROUND: Men with low- to intermediate-risk prostate cancer are typically asked to choose from a variety of treatment options, including active surveillance, radical prostatectomy, or brachytherapy. The Prolaris cell cycle progression test is intended to provide additional information on personal risk status to assist men with prostate cancer in their choice of treatment. To assist with assessing that new technology, this report synthesizes qualitative research on how men with prostate cancer use information to make decisions about treatment options. METHODS: We performed a systematic review and qualitative meta-synthesis to retrieve and synthesize findings across primary qualitative studies that report on patient perspectives during prostate cancer treatment decision-making. RESULTS: Of 8,610 titles and abstracts reviewed, 29 studies are included in this report. Most men diagnosed with prostate cancer express that their information-seeking pathway extends beyond the medical information received from their health care provider. They access other social resources to attain additional medical information, lived-experience information, and medical administrative information to help support their final treatment decision. Men value privacy, trust, honesty, control, power, organization, and open communication during interactions with their health care providers. They also emphasize the importance of gaining comfort with their treatment choice, having a chance to confirm their health care provider's recommendations (validation of treatment plan), and exercising their preferred level of independence in the treatment decision-making process. CONCLUSIONS: Although each prostate cancer patient is unique, studies suggest that most patients seek extensive information to help inform their treatment decisions. This may happen before, during, and after the treatment choice is made. Given the amount of information patients may access, it is important that they also establish the trustworthiness of the various types and sources of information. When information conflicts, patients may be unsure about how to proceed. Open collaboration between patients and their health care providers can help patients manage and navigate their concerns so that their values and perspectives are captured in their treatment choices.
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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 | 0.094 | 0.235 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".