Factors affecting surgical wait times for breast reconstruction
Bibliographic record
Abstract
OBJECTIVE: To examine factors that affect wait times for women seeking breast reconstruction at a Canadian academic centre. METHODS: A retrospective audit of 57 women seeking breast reconstruction over a three-year period was completed. Comparisons of wait times were made considering the surgical pathology, timing of reconstruction (immediate versus delayed), urgency of pathology, method of reconstruction (implant versus autologous) and the number of surgeons involved. Specifically, the wait times from referral to specialist consultation, consultation to surgery, and referral to surgery were examined. RESULTS: WOMEN WITH ACTIVE CANCER (DUCTAL CARCINOMA IN SITU: 43 days, invasive cancer: 40 days) had shorter wait times compared with those who had no active cancer (benign/high risk: 242 days, previously treated cancer: 343 days) (P<0.05). Women seeking delayed reconstruction had longer wait times (359 days) from referral to surgery than women seeking immediate reconstruction (98 days) (P<0.0001). Women seeking reconstruction at the time of mastectomy, with benign/high-risk disease, waited longer (242 days) than those with ductal carcinoma in situ (43 days) or invasive cancer (40 days) (P<0.001). Wait times for autologous free tissue transfer (213 days) were not significantly longer compared with implant reconstruction (116 days) (P=0.27). Women with acute cancer experienced similar wait times for implant reconstruction (44 days) as for a free tissue transfer (56 days) (P=0.46). Women with no acute cancer had similar wait times for implant (239 days) as free tissue transfer (369 days) (P=0.25). Patients requiring only plastic surgeons involved in the reconstructive effort waited longer (one surgeon: 299 days, two surgeons: 550 days) than patients requiring either two plastic surgeons and one general surgeon (130 days) or one plastic surgeon and one general surgeon (82 days) (P<0.05). Although more coordination is required with three surgeons, this is frequently associated with a diagnosis of acute cancer and, therefore, wait times are shorter.
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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 | 0.001 |
| 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) | 0.001 | 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".