Emergency Room Visits and Hospital Admission Rates After Curative Chemotherapy for Breast Cancer
Bibliographic record
Abstract
PURPOSE: Curative chemotherapy for breast cancer is associated with significant toxicities including emergency room (ER) visits and hospital admissions (HAs), events that are underreported in clinical trials. This study examined the reasons for, and factors associated with, ER visits and HA after curative chemotherapy for breast cancer in a tertiary Ontario hospital. PATIENTS AND METHODS: A retrospective study of all patients who completed at least one cycle of curative chemotherapy for breast cancer in 2011 and 2012 was conducted. We recorded ER visits and HAs within 30 days of any chemotherapy. We collected demographics, comorbidities, surgical data, tumor characteristics, chemotherapy type and cycles, and use of granulocyte colony-stimulating factors (G-CSF). RESULTS: A total of 149 patients underwent curative chemotherapy. Mean age was 58.6 years. Adjuvant chemotherapy was received by 85% of patients and G-CSF by 88.6%. At least one ER visit occurred in 53% of patients, and 13% required HA. The most common causes of ER visits were fever without neutropenia (23.3%), pain (12.8%), and febrile neutropenia (9%). Stage of breast cancer was the only factor statistically significantly associated with ER visits (P = .045); tumor size (P = .019), adjuvant chemotherapy (P = .045), and lower number of chemotherapy cycles (P = .005) were significantly associated with HA. CONCLUSION: Future research should focus on identifying the patient, provider, and health system factors associated with ER visits and HAs after chemotherapy for breast cancer, to minimize them and lessen the burden on the health care system.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".