The Impact of Pre-Operative Breast MRI on Surgical Waiting Time
Bibliographic record
Abstract
PURPOSE: To assess the impact of pre-operative breast MRI on surgical waiting time, and to identify factors contributing to the delay. MATERIALS AND METHODS: A retrospective cohort study involving 1274 patients was conducted after obtaining institutional ethics review. Surgical candidates for newly diagnosed breast cancer from 2007 to 2013 at a tertiary center were divided into 2 groups: those who had pre-operative MRI and those who did not. Linear regression using matched populations was used to compare the surgical waiting times, defined as time from the date of the first positive biopsy to the date of surgery. Potential influences on surgical waiting time and subgroup analysis were obtained using median regression analysis and the Kruskal-Wallis test. RESULTS: Mean surgical waiting time was 57.9 days (95% CI: 55.6-60.1) for MRI patients, compared to 46.8 days (95% CI: 45.1-48.9) for the control group, after matching for potential confounding factors (p<0.0001). Increased surgical waiting time was associated with more favorable pathology, later year of diagnosis, older patient age, surgeon and summer time. Second-look ultrasound and subsequent biopsies were associated with increased waiting time (p = 0.001). CONCLUSIONS: Pre-operative breast MRI increased surgical waiting time by 11 days using a conventional average of differences, and by 12 days after using a full matching statistical method (p<0.0001), with the main contributor being additional post-MRI procedures and imaging.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".