Capturing longer term surgical outcome measures as part of routine care of breast cancer patients
Bibliographic record
Abstract
INTRODUCTION: The transition away from routine clinical follow up after breast cancer towards imaging surveillance and patient-initiated contact limits opportunities for patients and doctors to communicate about the long-term effects of treatment. The ABS oncoplastic guidelines (2021) recommend that post-operative 2D images and patient-reported outcomes (PROMs) are routinely collected but give no guidance as to how best to implement this. METHODS: From December 2019 until March 2024, women due for their year 3 or 5 surveillance mammogram at The Royal Marsden Sutton site were invited to complete a BREAST-Q questionnaire and attend medical photography. Panel assessment of photographs was undertaken. Results were presented to the oncoplastic MDT, including summary PROMs and illustrative case presentations. Free-text comments were shared with the relevant teams. Associations between demographic or clinic-pathological factors and uptake were investigated. RESULTS: Of the 1211 women invited, 246 patients (20.3 %) completed BREAST-Q questionnaires, 182 (15.0 %) attended for medical photography and 114 (9.4 %) completed both. Uptake was not associated with age, ethnicity or surgical factors but patients with higher BMI were less likely to respond to the questionnaire. Patients who had undergone complex oncoplastic procedures were more likely to respond than those who had simple procedures. Patient-reported outcome results were in line with the published literature. CONCLUSION: Reviewing images with their paired PROMs and discussing free-text feedback was instructive for the team. Work is needed to identify barriers to patient participation and improve uptake to be representative of the overall patient population. Quantifying appearance in photographs would help summarise aesthetic outcome data.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".