Feasibility and acceptability of patient-reported outcomes data collection for clinical care following breast reconstruction.
Bibliographic record
Abstract
9042 Background: To date, systematic measurement of patient-reported outcomes (PROs) has played an important role in cancer research, but not in routine clinical care. Our objective was to evaluate the feasibility of developing and piloting an electronic PRO data collection in clinical care among breast reconstruction patients using the BREAST-Q, a previously developed condition-specific PRO measure for breast surgery patients that measures quality of life (e.g. psychosocial, physical and sexual well-being) as well as patient satisfaction (e.g. satisfaction with breasts, with information, with surgeon). Methods: The BREAST-Q was loaded to the MSKCC WebCore, a generic electronic patient-reporting platform adhering to strict privacy and security standards. Patients attending visits at the MSKCC Breast Reconstruction Clinic were asked to complete the BREAST-Q electronically prior to scheduled visits. For patients with email addresses, a reminder with web-link to the questionnaire was emailed automatically prior to the visit. Results: Over a 9 month start-up period, BREAST-Q surveys were completed by 1442 patients. Patients completed the questionnaire at set time points before and after surgery. A total of 2340 BREAST-Q surveys were completed overall. Mean completion time was 5:53 minutes. Acceptability was high with both patients and clinical staff contributing positive comments along with suggestions for improvement via email. Conclusions: This pilot experience suggests that ePRO data can be efficiently collected among outpatient breast surgery patients with high acceptability. In the next phase of this project, we will introduce real-time individual patient reports to the clinical team and evaluate the impact of this information on clinical care and quality improvement.
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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 | 0.130 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".