Risk factors for bladder adverse events following radiotherapy for localised and locally advanced prostate cancer in Gabon
Bibliographic record
Abstract
Abstract Background: The aim of this study was to identify potential risk factors for acute and late genitourinary toxicities and to determine, using a logistic regression model, which of these factors are also significant and robust predictors of these toxicities. Methods: We conducted a retrospective study by analysing the patient records and their treatment plans from 2013 to 2021. In total, a cohort of 46 patients with clinically staged cT1c-T4N0-1M0 prostate adenocarcinoma was treated with three-dimensional conformal radiotherapy (3D-CRT) with doses ranging from 66 to 80 Gy. Post-radiotherapy genitourinary toxicities were classified and graded according to the Common Terminology Criteria for Adverse Events (CTCAE v4.0). Results: Median follow-up was 57·5 months (range: 39 – 88 months). In univariable analysis, patient age ( p = 0·040), the prostate volume ( p = 0·0423), the clinical prostate volume irradiated at the prescribed dose ( p = 0·029) and the volume of the bladder receiving doses varying from 60 to 70 Gy were correlated with acute GU toxicities. Arterial hypertension (p = 0·022), some pre-existing urinary symptoms, a history of catheterisation ( p = 0·044) and acute genitourinary toxicity (p = 0.009) were linked to late genitourinary toxicities. The logistic regression model found that the prostate volume ( p = 0·0423) and the clinical prostate volume irradiated at the prescribed dose ( p = 0·029) were predictive of acute GU toxicity. Hypertension ( p = 0·039) and acute toxicities were predictive of late GU toxicity. Conclusion: The results of our study showed that it is essential to identify patients at risk of toxicities from the start of radiotherapy and to offer more proactive monitoring and management.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".