Impact of Surgical Approach on Patient-Reported Outcomes after Radical Prostatectomy: A Propensity Score-Weighted Analysis from a Multicenter, Prospective, Observational Study (The Pros-IT CNR Study)
Bibliographic record
Abstract
BACKGROUND: To report health-related quality of life outcomes as assessed by validated patient-reported outcome measures (PROMs) after radical prostatectomy (RP). -Methods: This study analyzed patients treated with RP within The PROState cancer monitoring in Italy, from the National Research Council (Pros-IT CNR). Italian versions of Short-Form Heath Survey and university of California los Angeles-prostate cancer index questionnaires were administered. PROMs were physical composite scores, mental composite scores and urinary, bowel, sexual functions and bothers (UF/B, BF/B, SF/B). Baseline unbalances were controlled with propensity scores and stabilized inverse weights; differences in PROMs between different RP approaches were estimated by mixed models. RESULTS: Of 541 patients treated with RP, 115 (21%) received open RP (ORP), 90 (17%) laparoscopic RP (LRP) and 336 (61%) robot-assisted RP (RARP). At head-to-head -comparisons, RARP showed higher 12-month UF vs. LRP (interaction treatment * time p = 0.03) and 6-month SF vs. ORP (p < 0.001). At 12-month from surgery, 67, 73 and 79% of patients used no pad for urinary loss in ORP, LRP and RARP respectively (no differences for each comparison). Conversely, 16, 27 and 40% of patients declared erections firm enough for sexual intercourse in ORP, LRP and RARP respectively (only significant difference for ORP vs. RARP, p = 0.0004). CONCLUSIONS: Different RP approaches lead to significant variations in urinary and sexual PROMs, with a general trend in favour of RARP. However, their clinical significance seems limited.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".