Monitoring acute care utilization (ACU) during adjuvant chemotherapy for early breast cancer (EBC) as a measure of quality.
Bibliographic record
Abstract
220 Background: Serious chemotherapy associated (CA) toxicities resulting in ACU are a concern when administering adjuvant chemotherapy for EBC. Monitoring ACU during adjuvant chemotherapy may serve as a measure of quality of cancer care delivery. We undertook a population based study of ACU in patients undergoing adjuvant chemotherapy for EBC compared with controls. Methods: All EBC patients diagnosed 01/07 – 12/09 in Ontario, Canada were identified from the Ontario Cancer Registry. Patient records were linked deterministically to provincial healthcare databases. All patients received ≥1 cycle of adjuvant chemotherapy. EBC cases (n = 4,718) were matched to non-cancer controls (n = 4,718) on age and geographic location. ACUs (emergency room or hospitalizations) within 30 days of chemotherapy were identified. If the primary reason for visit was a common toxicity of chemotherapy, the visit was considered chemotherapy associated (CA). All cause and CA visits were compared between cases and controls. Logistic regression models were used to identify covariates associated with ACU. Results: ACU was significantly higher in EBC pts compared with controls for both all cause (42.1% vs. 9.1%, p<.001) and CA (30.7% vs. 2.4%, p<.001) visits. Fever was the most common CA toxicity (22.9% vs. 1.2%,p<.001). Taxanes were significantly associated with increased ACU compared with anthracycline only (see Table). Conclusions: Serious chemotherapy associated toxicity resulting in ACU is common among EBC patients receiving chemotherapy. Interventions aimed at mitigating CA toxicity, particularly with the use of taxanes may reduce ACUs and improve quality of care. [Table: see text]
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".