Development and validation of a cycle‐specific risk score for febrile neutropenia during chemotherapy cycles 2–6 in patients with solid cancers: The <sup>CSR</sup>FENCE score
Bibliographic record
Abstract
The absolute risk reduction by prophylaxis in chemotherapy-induced febrile neutropenia (FN) is largest in patients at highest underlying risk. Therefore, reliable predictive models are needed. Here, we develop and validate such a model for risk of FN during chemotherapy cycles 2-6. A prediction score for risk of FN during the first cycle has recently been published. Patients with solid cancers initiating first-line chemotherapy in 2010-2016 were included. Cycle-specific risk factors were assessed by Poisson regression using generalized estimating equations and random split sampling. The derivation cohort included 4,590 patients treated with 15,419 cycles, wherein 326 (2.1%) FN events occurred. Predictors of FN in multivariable analyses were: higher predicted risk of FN in the first cycle, platinum- or taxane-containing therapies, concurrent radiotherapy, treatment in cycle 2 compared to later cycles, previous FN or neutropenia and not receiving granulocyte colony-stimulating factors. Each predictor added between -2 and 8 points to each patient's score (median score 4; interquartile range, 1-6). The incidence rate ratios for developing FN in the intermediate (score 1-4), high (score 5-6) and very high risk groups (score ≥7) were 7.8 (95% CI, 2.4-24.9), 18.6 (95% CI, 5.9-58.8) and 51.7 (95% CI, 16.5-162.3) compared to the low risk group (score ≤0), respectively. The score had good discriminatory ability with a Harrell's C-statistic of 0.78 (95% CI, 0.76-0.80) in the derivation and 0.75 (95% CI, 0.72-0.78) in the validation cohort (patient n = 2,295, cycle n = 7,670). The Cycle-Specific Risk of FEbrile Neutropenia after ChEmotherapy score is the first published method to estimate cycle-specific risk of FN.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".