The Impact of a Digital Health Pathway on Complications Following HIFU Treatment in Prostate Cancer Patients—A Pre- and Postintervention Study
Bibliographic record
Abstract
Background/Objectives: Digital health pathways, including prehabilitation programs, may help reduce complications after urologic procedures. This study assesses the impact of a digital health intervention on infectious complications, urinary retention, and unplanned patient contacts after high-intensity focused ultrasound (HIFU) treatment for prostate cancer. Methods: A pre-/post-intervention study design was applied. The intervention consisted of implementing a mobile health pathway via a mobile application integrated into the perioperative management of patients undergoing HIFU treatment for prostate cancer. Urinary complication rates and unplanned patient contacts with the surgical team before and after implementation were compared using the Mann–Whitney U test. Results: 58 patients were included in the analysis. Demographic, tumor, and treatment characteristics were comparable between both groups. The post-intervention group showed a lower incidence of symptomatic urinary infections (3 vs. 10; p = 0.019) and fewer unplanned visits (4 vs. 10; p = 0.047) after the implementation of the mobile application. No significant differences in rates of acute urinary retention and unplanned communication with the surgical team were observed. Conclusions: Integration of a digital health pathway was associated with reduced infectious complications and fewer unplanned visits after HIFU treatment. Incorporating such tools into perioperative management may improve patient outcomes.
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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 | 0.002 | 0.007 |
| 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) | 0.003 | 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 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".