Prediction of acute urinary retention after I-125 prostate brachytherapy
Bibliographic record
Abstract
Summary Prostate cancer is the most common type of cancer in men in the Western world. For localized prostate cancer, I-125 prostate brachytherapy is an adequate treatment option. The median 10-years survival after treatment is 95%. Since outcomes are excellent, long-term quality of life (QOL) is considered an important endpoint as well. We evaluated long-term QOL up to six years after treatment. Results showed that after an initial worsening (at 1 month after treatment), HRQOL gradually improved and returned to baseline values at 1 year after treatment. Subsequently, HRQOL scores stayed stable up to 6 years after treatment. However, acute urinary retention (AUR) (the most important severe adverse event after prostate brachytherapy) might have a negative impact on the patient’s QOL. If AUR is present, prolonged catheterization is often required. We found that patients who developed AUR (10%) had, indeed, a significantly worse QOL compared to patients without AUR. Therefore, we purposed to identify predictors for AUR. In a group of 714 patients, we extensively examined which patient- and treatment factors were associated with the development of AUR. We found that the most important pre-treatment predictors for AUR were: prostate volume, IPSS score, neo-adjuvant hormonal treatment and the extent of prostate protrusion. There was relation between radiation dose to the prostate and the risk of AUR. The final aim of the thesis was to develop a clinical nomogram to pre-operatively predict the risk of AUR after I-125 prostate brachytherapy. The developed nomogram was based on the above mentioned risk factors. By simply counting the sum-score in the nomogram, the risk of AUR can be read off. The discriminative value of the nomogram was high (ROC 0.82) and calibration measures were good. The nomogram was externally validated on patient data from the Princess Margarett Hospital, Toronto. External validation of the nomogram shows adequate discrimination of patients with and without AUR. The developed nomogram might be a useful tool for patient management and -counseling to all physicians performing I-125 prostate brachytherapy, and might aid in individualized treatment decision making in patients with localized prostate cancer.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".