Serial personal digital assistant data capture of health-related quality of life: A randomized controlled trial in a prostate cancer clinic
Bibliographic record
Abstract
BACKGROUND: In clinical and research practice linked to prostate cancer treatment, frequent monitoring of patient health-related quality of life (HRQOL) is essential. Practical and analytic limitations of paper questionnaire data capture may be overcome with the use of self-administered personal digital assistant (PDA) data collection. The objective of this study was to assess the reliability, validity, and feasibility of using PDA in place of paper versions of the International Prostate Symptom Score (IPSS), the Patient Oriented Prostate Cancer Utility Survey (PORPUS), and the International Index of Erectile Function-5 (IIEF-5) in a prostate cancer clinic setting. METHODS: 152 participants were randomly assigned to one of three conditions: 1) paper followed by PDA survey; 2) PDA followed by paper survey; or 3) PDA followed by PDA survey. Evaluation included an assessment of data quality (internal consistency, test-retest reliability, response correlation, completeness of data), and feasibility (participation rates, time to completion, preference and difficulty/ease of using PDA). RESULTS: Internal consistency was similar for both PDA and paper applications. Test-retest reliability was confirmed for PDA repeated administration. Data from paper and PDA questionnaires were strongly correlated. Lower missed item rates were found in PDA administration. 82.8% of participants preferred using the PDA or had no preference. Mean difficulty/ease ratings indicated that participants found the PDA easy to use. Age did not significantly correlate with preference or difficulty. CONCLUSION: The results confirm the adaptability of the IPSS, IIEF-5, and the PORPUS to PDA administration. Similarly, the findings of this study support the feasibility of using PDA technology for HRQOL serial data capture in the prostate cancer patient population.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".