Review of IPSS Questionnaire in Postoperative Transurethral Resection of Prostate (TURP) for Streamlining Follow-up Protocols
Bibliographic record
Abstract
Objective Clarify the role of IPSS questionnaire for post TURP operation patients, to assess and streamline best follow-up protocols Materials and Methods We identified 87 consecutive patients over 6 months undergoing standardized bipolar TURP. We retro-spectively reviewed patients at 3 months in follow-up clinic, where we performed tests including Qmax, Post-void residual (PVR) and IPSS (International Prostate Symptom Score). We identified patients who were discharged or underwent a change in standard management at this point, and used ROC (Receiver Operating Curve) curve analysis to identify the tools which showed the best ability to predict this decision. Results ROC curve analysis suggested Qmax (AUC: 0.7751) and IPSS (AUC 0.8571) were the best tools to predict a change in management. Given the IPSS tool is a questionnaire, thus holding most promise to streamline protocols, we applied Youden-J test to show IPSS=8 cut-off was best to identify management changes. Conclusion The IPSS tool is able to predict a need for change in management in post TURP patients at 3 months. This will allow a simple triage system to provide an efficient and effective decision-making process for discharge without the need for clinic attendance.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".