“Low-Risk” Prediction Rule for Pediatric Oncology Patients Presenting With Fever and Neutropenia
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Bibliographic record
Abstract
PURPOSE: To prospectively derive and validate a clinical prediction rule to allow a more tailored approach to the management of pediatric oncology outpatients presenting with fever and neutropenia. PATIENTS AND METHODS: The clinical prediction rule was derived over a 1-year period and then validated over the following 8 months in a new set of fever and neutropenia episodes. Patients were excluded if they presented with comorbidity or an abnormal chest x-ray (CXR). RESULTS: Significant bacterial infection (SBI; defined as any blood or urine culture positive for bacteria, interstitial or lobar consolidation on CXR, or unexpected death from infection) was documented in 43 of the 227 episodes. Multivariate analysis found four significant factors: bone marrow disease, general appearance unwell on initial examination, monocyte count less than 0.1 x 10(9)/L, and peak oral or oral equivalent temperature greater than 39 degrees C. Only the monocyte count contributed to determining a low-risk group, excluding SBI with 84% sensitivity (95% confidence interval [CI], 61% to 100%), 42% specificity (95% CI, 38% to 46%), and a negative predictive value of 92% (95% CI, 76% to 100%). If the monocyte count was >/= 0.1 x 10(9)/L at the time of presentation (low risk), the incidences of SBI and bacteremia were 8% and 5%, respectively, versus 25% and 17% in the high-risk group. When validated in a new population of 136 episodes of fever and neutropenia, the incidences of SBI and bacteremia in the low-risk group were 12% and 5%, respectively, and 25% and 19% in the high-risk group. CONCLUSION: Pediatric oncology outpatients with fever and neutropenia who present with an initial monocyte count of >/= 0.1 x 10(9)/L and do not have comorbidity or an abnormal CXR at the time of presentation are at lower risk for SBI and can be considered for less aggressive initial therapy.
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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) | 0.001 | 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 | 0.001 |
| 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 it