P262 How patients prioritize features of different potential treatment pathways in early breast cancer: a best-worst scaling study
Bibliographic record
Abstract
Methods: The breast unit treats over 400 breast cancer patients per year.The unit treats symptomatic and screening patients, covering a wide geographical area in Southwest Wales.Data was collected over a 12/12 period.The cohort included all patients attending for mammographically-led follow up between September 2022 and September 2023.Exclusions included patients less than one year post-treatment.Duplicate data was also removed.Patient electronic records were accessed to obtain information on initial diagnoses, treatment, clinic attendance, recalls, follow-up and outcomes.Results: From the sample group of 110 patients; 4 patients (3.6%) were recalled following mammographic follow up for review.The majority of patients in the cohort were treated for either ductal carcinoma in-situ (53/110, 48.1%) or early stage invasive ductal carcinomas (53/110, 51.8%). 2 patients from the sample group had died at the time of data collection, with one of these deaths related to the original cancer.Rates of clinic attendance varied widely between patients, with 14.5% (16/110) attending clinic in addition to their mammogram follow up every year.75% (12/16) of these appointments were requested by patients, in the main for symptoms side effects from endocrine therapy.The remaining patients 25% (4/16) were recalled for further assessment following mammography, with sonography and clinical examination being normal.Conclusion(s): These results seem to support the introduction of mammogram led follow up.The findings suggest that mammogramonly reviews are acceptable to patients, with the provision of clinic assessment for individuals if needed.In addition, this practice has increased capacity for new patient clinics in the unit.Further data collection regarding patient preferences and reasons behind seeking additional clinical input would be useful in the future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".