Population-Based Assessment of Emergency Room Visits and Hospitalizations Among Women Receiving Adjuvant Chemotherapy for Early Breast Cancer
Bibliographic record
Abstract
PURPOSE: Adjuvant chemotherapy is considered standard care for patients with lymph node (LN) -positive and high-risk LN-negative early breast cancer (EBC). Although chemotherapy-associated toxicities are documented in clinical trials, the impact of toxicities on emergency room (ER) visits and hospitalizations (ER + Hs) at a population level with contemporary chemotherapy is unknown. We undertook a population-based study of ER + Hs in patients with EBC receiving adjuvant chemotherapy compared with noncancer controls (NCCs). METHODS: All patients diagnosed with EBC between January 2007 and December 2009 in Ontario, Canada, were identified from the Ontario Cancer Registry. Patient records were linked deterministically to provincial health care databases to provide comprehensive medical follow-up. All patients received ≥ one cycle of adjuvant chemotherapy. Patient cases of EBC (n = 8,359) were matched to NCCs (n = 8,359) on age, comorbidity, and geographic location. ER + Hs within 30 days of chemotherapy were identified. If the primary reason for the visit was a common chemotherapy toxicity, the visit was considered chemotherapy associated. All-cause and chemotherapy-associated visits were compared between patient cases and controls. Logistic regression models were used to identify covariates associated with ER + Hs. RESULTS: The proportion of patients with at least one ER + H was significantly higher in patients with EBC undergoing chemotherapy compared with NCCs (43.4% v 9.4%; P < .001). Patients with EBC were also more likely to have multiple ER + Hs (17.9% v 2.4%; P < .001). On multivariable analysis, comorbidity, receiving a regimen containing docetaxel, and certain geographic regions were associated with increased odds of ER + Hs. CONCLUSION: ER + Hs are common among patients with EBC receiving chemotherapy and significantly higher than among controls. This represents a potential opportunity for quality improvement.
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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 | 0.002 |
| 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 | 0.000 |
| 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 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".