P5-18-02: A Population Level Assessment of Emergency Room Visits and Hospitalizations for Women Undergoing Adjuvant Chemotherapy for Early Breast Cancer.
Bibliographic record
Abstract
Abstract Background: Adjuvant chemotherapy is considered the standard of care for women with lymph node positive and high risk lymph node negative breast cancer. While the acute toxicities of chemotherapy are well documented in clinical trials, the frequency of serious treatment related toxicities of adjuvant chemotherapy in the general population is not well described. We undertook a population based assessment of the frequency of serious treatment related toxicity in women undergoing adjuvant chemotherapy for early breast cancer (EBC). Methods: All incident EBC patients diagnosed between January 2007 and December 2008 in Ontario, Canada were identified from the Ontario Cancer Registry. Patient records were linked deterministically to multiple provincial administrative health care databases to provide comprehensive medical follow-up. Exclusion criteria were set to exclude patients on chemotherapy for advanced breast cancer. Any patient with who received at least 1 cycle of adjuvant chemotherapy was included in the analysis. Serious toxicities resulting in emergency room (ER) visits or hospitalizations occurring between the start date of chemotherapy and 30 days after the last dose of chemotherapy were identified. Logistic regression models were used to identify the impact of chemotherapy regimen, age, comorbidity and duration on therapy on the likelihood of experiencing serious toxicity. Results: Of the 3090 women identified in our cohort, 1440 (46.6%) experienced at least 1 serious toxicity resulting in an ER visit during their adjuvant treatment. Of the ER visits, the majority (1107, 87%) were attributable to treatment related toxicities. Febrile neutropenia (FN) was the most common treatment related toxicity occurring in 27.1% of patients in the cohort. Docetaxel containing regimens were associated with a significantly higher rate of ER visits and FN (54.6%, 34.6%) compared with paclitaxel (38.0%, 17.9%), or anthracycline alone (epirubicin 45.8%, 23.8%; doxorubicin 32.8%, 15.4%). Table 1 displays the impact of clinical and patient factors on multivariable analysis. Conclusion: Serious toxicities are a common in women undergoing adjuvant chemotherapy for EBC and result in significant acute health care utilization. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P5-18-02.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".