Determining issues of importance for patients with prostate cancer: Results of a web-based study in 2,128 patients with prostate cancer for the development of a quality of life (QL) instrument, the prostate cancer symptom scale (PCSS)
Bibliographic record
Abstract
5138 Background: Identifying key issues for patients with malignancy is central to assessing QL and patient reported outcomes. This aids in evaluating the effectiveness of treatment programs for those with the disease. The immediate aim of this study was to determine content validity using a large patient panel for the PCSS, a QL measure for patients with prostate cancer. The PCSS also uses an inexpensive hand-held pocket PC to enhance feasibility. The PCSS concept is based on the LCSS (a validated lung cancer instrument). Methods: We used the established patient base of the web-based NexCura patient information resource to survey registered patients with prostate cancer. Demographic stratifications included stage of disease, prior radical prostatectomy, and current treatment (none, hormonal, non-hormonal). 2,128 patients completed the anonymous web-conducted survey, performed over a 3-day period. Patients were asked to rank 18 issues on a 5-point scale assessing the importance of each item. Issues included general, prostate-specific, psychosocial and summative items. Results: The 10 highest (and 2 lowest) ranked items are seen in the table ; results are described by the percent of patients choosing the top category (very important) and the top 2 rating categories of importance. Ratings by disease subsets (such as NED or metastatic disease; hormonal or non-hormonal treatment) were quite similar to results found for the whole group, as listed in the table . Conclusions: These results represent the largest survey of patient concerns in prostate cancer and support using computer-assisted survey technology to assess such information in all malignancies to obtain patient input rapidly from large patient samples. Strong support for content validity for the PCSS was obtained. [Table: see text] No significant financial relationships to disclose.
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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 | 0.003 | 0.008 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".