Predicting fever in neutropenia with safety‐relevant events in children undergoing chemotherapy for cancer: The prospective multicenter SPOG 2015 FN Definition Study
Bibliographic record
Abstract
BACKGROUND: Fever in neutropenia (FN) remains a frequent complication in pediatric patients undergoing chemotherapy for cancer. Preventive strategies, like primary antibiotic prophylaxis, need to be evidence-based. PROCEDURE: Data on pediatric patients with any malignancy from the prospective multicenter SPOG 2015 FN Definition Study (NCT02324231) were analyzed. A score predicting the risk to develop FN with safety-relevant events (SRE; bacteremia, severe sepsis, intensive care unit admission, death) was developed using multivariate mixed Poisson regression. Its predictive performance was assessed by internal cross-validation and compared with the performance of published rules. RESULTS: In 238 patients, 318 FN episodes were recorded, including 53 (17%) with bacteremia and 68 (21%) with SRE. The risk-prediction score used three variables: chemotherapy intensity, defined according to the expected duration of severe neutropenia, time since diagnosis, and type of malignancy. Its cross-validated performance, assessed by the time needed to cover (TNC) one event, exceeded the performance of published rules. A clinically useful score threshold of ≥11 resulted in 2.3% time at risk and 4.1 months TNC. Using external information on efficacy and timing of intermittent antibiotic prophylaxis, 4.3 months of prophylaxis were needed to prevent one FN with bacteremia, and 5.2 months to prevent one FN with SRE, using a threshold of ≥11. CONCLUSIONS: This score, based on three routinely accessible characteristics, accurately identifies pediatric patients at risk to develop FN with SRE during chemotherapy. The score can help to design clinical decision rules on targeted primary antibiotic prophylaxis and corresponding efficacy studies.
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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 | 0.001 |
| 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".