Wait Times for Breast Cancer Surgery: Effect of Magnetic Resonance Imaging and Preoperative Investigations on the Diagnostic Pathway
Bibliographic record
Abstract
PURPOSE: Women with breast cancer often require an extensive diagnostic work-up. We sought to determine the overall wait time, from the patient's perspective, from identification of an imaging abnormality to definitive treatment. The objective was to identify which factors contribute to overall wait time in women with breast cancer. METHODS: A retrospective chart review in a tertiary care center was performed to identify all women who had breast surgery for invasive carcinoma and ductal carcinoma in situ. We recorded the dates of first imaging abnormality, first biopsy, subsequent imaging and biopsy, first consultation with any physician at the cancer center, first surgical consultation, and date of surgery. Clinical data that might influence these dates were then extracted. Wait times were calculated and factors associated with wait times were described. RESULTS: Eligible consecutive women with a cancer diagnosis (n = 264) were identified. The median time between first imaging abnormality and definitive surgery was 79 days. The median time from first surgical consultation to surgery was significantly longer in women who underwent magnetic resonance imaging and in women who underwent initial imaging outside of our tertiary care center (P < .05). On multivariable analysis, the modifiable factors associated with prolonged wait times included number of preoperative clinic visits, number of visits to radiology, and initial imaging outside of our center (P < .05). CONCLUSION: Extensive diagnostic work-up is an important factor that affects the time to definitive surgery. A more integrated approach using a rapid diagnostic clinic for tissue diagnosis initially, followed by facilitated preoperative evaluation, may potentially decrease wait times in breast evaluation.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".