Can Chemotherapy-Related Acute Care Visits Be Accurately Identified in Administrative Data?
Bibliographic record
Abstract
PURPOSE: There is increasing interest in using administrative data to examine treatment-related complications that lead to emergency department (ED) visits or hospitalizations (H). The purpose of this study was to evaluate the reliability of billing codes for identifying chemotherapy-related acute care visits (CRVs) among women with early-stage breast cancer. MATERIALS AND METHODS: The cohort was identified by using deterministically linked health databases and consisted of women who were diagnosed with early-stage breast cancer who started adjuvant chemotherapy between 2007 and 2009 in Ontario, Canada. A random sample of 496 patient cases was chosen as the validation cohort. Sensitivity (SN) and specificity (SP) were calculated for three scenarios: chemotherapy-related ED visit, chemotherapy-related H, and febrile neutropenia (FN)-related visit. For FN-related visits, three definitions were considered: general, moderate, and strict. RESULTS: The administrative cohort consisted of 8,359 patients, 43.4% of whom had at least one ED or H, including 1,496 women who had multiple visits that resulted in 6,293 unique visits. Of these, 73.1% were considered CRVs. The algorithm performed well in identifying CRVs that included H either from ED (SN, 90%; SP, 100%) or directly from home (SN, 91%; SP, 93%), but less well for ED visits that did not result in H (SN, 65%; SP, 80%). Depending on which FN algorithm was used, 4.8% to 24% of visits were considered related. The moderate FN algorithm provided the best tradeoff between SN (69% to 97%) and SP (83% to 98%). CONCLUSION: Administrative data can be valuable in evaluating chemotherapy-related serious events. Algorithm validation in other cohorts is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".