Identifying Patients at High Risk for Neutropenic Complications During Chemotherapy for Metastatic Breast Cancer With Doxorubicin or Pegylated Liposomal Doxorubicin
Bibliographic record
Abstract
OBJECTIVE: To develop a cycle-based risk prediction model for neutropenic complications (NC) during chemotherapy with doxorubicin (DOX) or a pegylated liposomal formulation (PLD) for patients with metastatic breast cancer (MBC). METHODS: Data analyzed was from a phase III, randomized clinical trial of DOX (60 mg/m(2) every 3 weeks) or PLD (50 mg/m(2) every 4 weeks) for the first line therapy for MBC (n = 509) (O'Brien et al, Ann Oncol. 2004;15:440-449). NC were defined as an absolute neutrophil count < or =1.5 x 10(9) cells/L (ie, > or =grade II) before the next cycle, febrile neutropenia or neutropenia with a documented infection. Patient and hematologic factors potentially associated with NC were evaluated. Factors with a P value of < or =0.25 within a cycle were included in a generalized estimating equations regression model. Using backward elimination, we derived a risk scoring algorithm (range 0-63) from the final reduced model. RESULTS: Risk factors retained in the model included poor performance status, absolute neutrophil count < or =2.0 x 10(9) cells/L in the previous cycle, the first cycle of chemotherapy, DOX versus PLD and advanced age. A precycle risk score from > or =25 to <40 for a given patient was identified as being the optimal threshold for sensitivity (58.0%) and specificity (78.7%). Patients with a score at or beyond this threshold would be considered at high risk for developing NC in later cycles. CONCLUSION: The use of this model may enhance patient care by targeting preventative therapies (eg, granulocyte colony stimulating factor or PLD) to those MBC patients most likely to experience NC during anthracycline-based chemotherapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".