Use of the Prostate Cancer–specific Quality of Life Instrument (PROSQOLI) in clinical practice
Bibliographic record
Abstract
e20569 Background: Improvement of quality of life (QOL) is a major therapeutic goal for men with advanced prostate cancer (APC). The PROSQOLI consists of a series of 9 linear analog self-assessment (LASA) scales that evaluate pain, fatigue, appetite, constipation and other symptoms, and overall well-being; it was designed and validated for use in patients with APC. Here we evaluate the use of a computer-based version of the PROSQOLI in routine clinical practice for its ability to stimulate recognition of symptoms and for its impact on clinical decision-making. Methods: Consenting patients with APC completed a touch screen version of the PROSQOLI before seeing the doctor at visits to the outpatient clinic. In phase I of the study physicians did not have access to this information; in phase II physicians were provided with results of the PROSQOLI and its changes from previous visits. Physicians’ recognition of symptoms, and changes in management were extracted from transcribed clinical notes. Results: 36 men were recruited, and data collected from 120 clinic visits (85 phase I, 35 phase II). Normalized symptom scores (0=none; 100 =very severe) were highest for fatigue (median = 42), followed by urinary problems (27) and mood (27) with no differences between phases. Median normalized pain scores were 25 in phase I and 11 in phase II (p=0.03). Changes in management occurred in 41% of phase I, and 43% of phase II visits (NS). Comparison of patient-assessed and physician-described symptoms was limited by lack of documentation in transcribed notes: mention of PROSQOLI symptoms ranged from 75% of visits for pain to 2/120 visits for mood. Presence or absence of fatigue and urinary symptoms were described at 53% and 40% of visits respectively. Rates of documentation did not differ between study phases. Conclusions: No impact on patient care could be demonstrated as a result of computer-based self-assessment of changes in symptoms and QOL. Prostate cancer-specific symptoms were poorly documented in clinical notes; improved recording of symptoms might be facilitated by the use of a tool such as the electronic touch-screen PROSQOLI. No significant financial relationships to disclose.
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 | 0.020 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".