Predicting Neutropenia Risk in Breast Cancer Patients from Pre-Chemotherapy Characteristics
Bibliographic record
Abstract
A previous study (Pittman, Hopman, Mates) of breast cancer patients undergoing curative chemotherapy (CT) found that the third most common reason for emergency department (ER) visits and hospital admission (HA) was febrile neutropenia. Factors associated with ER visits and HA included (1) stage of the cancer, (2) size of tumor, (3) adjuvant versus neo-adjuvant CT (“adjuvance”), and (4) number of CT cycles. We hypothesized that a statistically-significant predictor of neutropenia could be built based on some of these factors, so that risk of neutropenia predicted for a patient feeling unwell during CT could be used in weighing need to visit the ER. The number of CT cycles was not used as a factor so that the predictor could calculate the neutropenia risk for a patient before the first CT cycle. Different models were built corresponding to different pre-chemotherapy factors or combinations of factors. The single factor yielding the best classification accuracy was tumor size (Mathews’ correlation coefficient φ = +0.18, Fisher’s exact two-tailed probability P < 0.0374). The odds ratio of developing febrile neutropenia for the predicted high-risk group compared to the predicted low-risk group was 5.1875. Combining tumor size with adjuvance yielded a slightly more accurate predictor (Mathews’ correlation coefficient φ = +0.19, Fisher’s exact two-tailed probability P < 0.0331, odds ratio = 5.5093). Based on the observed odds ratios, we conclude that a simple predictor of neutropenia may have value in deciding whether to recommend an ER visit. The predictor is sufficiently fast that it can run conveniently as an Applet on a mobile computing device.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".