Inter-institutional analysis demonstrates the importance of lower than previously anticipated dose regions to prevent late rectal bleeding following prostate radiotherapy
Bibliographic record
Abstract
Purpose To investigate whether inter-institutional cohort analysis uncovers more reliable dose–response relationships exemplified for late rectal bleeding (LRB) following prostate radiotherapy. Material and methods Data from five institutions were used. Rectal dose–volume histograms (DVHs) for 989 patients treated with 3DCRT or IMRT to 70–86.4 Gy@1.8–2.0 Gy/fraction were obtained, and corrected for fractionation effects ( α/β = 3 Gy). Cohorts with best-fit Lyman–Kutcher–Burman volume-effect parameter a were pooled after calibration adjustments of the available LRB definitions. In the pooled cohort, dose–response modeling (incorporating rectal dose and geometry, and patient characteristics) was conducted on a training cohort (70%) followed by final testing on the remaining 30%. Multivariate logistic regression was performed to build models with bootstrap stability. Results Two cohorts with low bleeding rates (2%) were judged to be inconsistent with the remaining data, and were excluded. In the remaining pooled cohorts ( n = 690; LRB rate = 12%), an optimal model was generated for 3DCRT using the minimum rectal dose and the absolute rectal volume receiving less than 55 Gy (AUC = 0.67; p = 0.0002; Hosmer–Lemeshow p -value, p HL = 0.59). The model performed nearly as well in the hold-out testing data (AUC = 0.71; p < 0.0001; p HL = 0.63), indicating a logistically shaped dose–response. Conclusion We have demonstrated the importance of integrating datasets from multiple institutions, thereby reducing the impact of intra-institutional dose–volume parameters explicitly correlated with prescription dose levels. This uncovered an unexpected emphasis on sparing of the low to intermediate rectal dose range in the etiology of late rectal bleeding following prostate radiotherapy.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".