Pain control and opioid use as a function of workflow in MRI-guided interstitial cervix brachytherapy
Bibliographic record
Abstract
PURPOSE: /Objective(s): Pain management during brachytherapy for cervix cancer is challenging. Institutional practice for brachytherapy delivery and pain management varies. Here we retrospectively assessed pain control and opioid use requirements during different MRI-guided interstitial cervix brachytherapy workflows. MATERIALS: /Methods: In this retrospective study, data was collected on ninety-one patients receiving MR-guided interstitial brachytherapy for cervix cancer between June 2022 and June 2024. Abstracted data included: demographics, disease characteristics, pain scores, opioid use, and brachytherapy workflow. Patients were either treated as in-patients or out-patients. In-patients remained overnight to receive a second fraction the following day. Out-patients received a single fraction and were discharged the same day. Out-patients were further divided into intra-operative versus post-operative treatment. For intra-operative treatment the entire procedure was performed under general anesthesia (GA). For post-operative treatment only applicator insertion was under GA. Multivariable linear regression modelling was used for analysis of opioid dose and pain scores. RESULTS: (standard deviation (SD) 19.4) and 92.2 Gy (SD 2.5), respectively. In-patient stay was associated with increased opioid requirements, higher average pain, and more episodes of uncontrolled pain (p < 0.001). Within those treated as out-patients, intra-operative treatment was associated with lower average pain and fewer episodes of uncontrolled pain (p < 0.001). CONCLUSION: In-patient treatment was associated with worse pain control, despite increased opioid use. Within those treated as out-patients, intra-operative treatment further improved pain management.
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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 | 0.002 | 0.011 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".