Measuring patient reported outcomes in brachytherapy: Why we should do it and more importantly how
Bibliographic record
Abstract
• A review of PROs in brachytherapy-especially prostate, gynaecologic and breast cancer. • To elaborate on the evidence that exists in the use of specific PROMs within prostate, breast and gynaecologic cancers. • Describing a novel rectal brachytherapy PROMs approach aiming to identify and resolve symptoms at an early stage. As the treatment for cancer improves and advances are made, the clinical focus is often on treatment response and survival. However, these are not the only factors which are important to patients. More patients are living longer after cancer treatment and therefore it is important that we can describe not only the treatment to patients but also what their life will be like during and after treatment. Patient reported outcomes (PROs) allow us to describe these. Although there are a range of patient reported outcome measures (PROMs) available to the clinician to assess these, the use of them in many areas of brachytherapy lags behind ideal levels. Brachytherapy has many features that differ to external beam radiotherapy (EBRT) yet the assessment of quality of life during and after treatment is much more scarce than EBRT. Brachytherapy is often used in the setting of organ preservation or in place of radical surgery, yet there is a paucity of quality of life data comparing the different treatment modalities. This review article will aim to elaborate on the evidence that exists in the use of specific PROMs within prostate, breast and gynaecologic cancers and describe the development of a novel PROMs approach in rectal brachytherapy which aims to identify and resolve symptoms at an early stage.
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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 | 0.064 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".