Personal Digital Assistant Data Capture: The Future of Quality of Life Measurement in Prostate Cancer Treatment
Bibliographic record
Abstract
PURPOSE: This article examines the potential use of personal digital assistant (PDA) data capture systems for real-time linear monitoring of health-related quality of life (HRQOL) in prostate cancer research and clinical care. METHODS: We discuss the benefits and potential issues of using PDA data capture in the clinical health care setting. In addition, we describe the development and potential use of a PDA data capture system specific to managing HRQOL in prostate cancer treatment. CONCLUSION: Follow-up health care clinics require a practical and systematic process of HRQOL data capture and analysis. Traditional paper questionnaire data capture is problematic. Data manipulation required for clinical decision-making is impractical for patient feedback on same-day clinic visits. Furthermore, the process of transforming paper questionnaire data to analysis-quality data can compromise data integrity. In contrast, research findings confirm the acceptability, ease of use, and reliability of PDAs in capturing data across health care settings, including the collection of serial HRQOL data. The main concern for PDA capture systems is the ability to compare respondent's answers between the paper and PDA questionnaire. Other challenges included patients reporting a lack of computer literacy and/or poor eyesight, as well as initial start-up costs. If issues are successfully addressed, the use of a PDA data capture system, such as the PDA HRQOL system at Princess Margaret Hospital's Prostate Centre, allows for valid and economical data collection with the possibility of linear real-time measurement of changes in HRQOL. Accordingly, there appears to be significant potential for PDA data collection of serial HRQOL in prostate cancer clinic settings.
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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 | 0.031 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".