Pad Weight, Pad Number and Incontinence-Related Patient-Reported Outcome Measures After Radical Prostatectomy
Bibliographic record
Abstract
Objectives: To evaluate the correlation between 3- and 6-week postoperative 24-hour pad weight, daily pad number, and the International Consultation on Incontinence Questionnaires for Male Lower Urinary Tract (ICIQ-MLUTS), ICIQ-Short Form (ICIQ-SF) and UCLA Prostate Cancer Index (UCLA-PCI) in patients undergoing robotic-assisted radical prostatectomy (RARP). Methods: This prospective study included patients undergoing RARP between February and November 2019. Patients completed a 24-hour pad test, assessing pad weight and number, and 3 validated patient-reported outcome measures (PROMs); the ICIQ-MLUTS, ICIQ-SF and UCLA-PCI, preoperatively and at 3 and 6 weeks postoperatively. Results: A total of 47 patients were included in the study. There was a strong correlation between 24-hour pad weight and the ICIQ-SF at 3 weeks (r = 0.71, P < 0.001) and 6 weeks (r = 0.68, P < 0.001). There was a strong correlation between 24-hour pad weight and ICIQ-MLUTS incontinence (r = 0.80, P < 0.01) and incontinence QoL burden (r = 0.79, P < 0.01) at 6 weeks. There was a moderate correlation between the 24-hour pad weight and UCLA-PCI urinary function (r = 0.58, P < 0.001) and urinary QoL burden (r = 0.66, P < 0.001) at 6 weeks. The correlation between pad number and 24-hour pad weight was weak at 6 weeks (r = 0.34, P < 0.001). Conclusion: PROMs may be used as a substitute for the 24-hour pad weight test for post-prostatectomy incontinence (PPI) assessments in the early post-RARP period. The ICIQ-SF and UCLA-PCI urinary function and QoL scores correlate with 24-hour pad weight. However, the ICIQ-MLUTS incontinence and QoL scores provide the strongest correlation with PPI.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".