Time to treatment and hospital visits for patients undergoing neoadjuvant chemotherapy for breast cancer in a single payer system
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Neoadjuvant chemotherapy (NAC) requires coordination of various services to ensure timely and accurate delivery of care. This can result in multiple hospital visits and extend time to treatment (TTT). The primary purpose of our study was to evaluate time to NAC for patients at a regional cancer centre. Healthcare resource use in the form of hospital visits before NAC was also evaluated. METHODS: A retrospective chart analysis of patients with invasive breast cancer who underwent NAC between 1 January 2012 and 31 December 2018 was performed. RESULTS: Overall, 286 patients underwent NAC. Median TTT was 22 days (range: 2-105). Median number of visits between first consultation and NAC was 5 (range: 0-12). Majority of additional visits were for diagnostic imaging/interventions, with a median number of 4 visits (range: 0-10). Each additional hospital visit increased time to NAC treatment by 14%. CONCLUSIONS: Women undergoing NAC require multiple visits before initiating treatment-the majority of these visits are for diagnostic imaging. These results support the need for the coordination of multidisciplinary care and diagnostic imaging for breast cancer patients undergoing NAC to reduce hospital visits, improve the patient experience, and reduce TTT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".