Does age play a role in fever and neutropenia events and complications: A comparison of adolescents versus younger children with cancer at a tertiary care pediatric hospital, a pilot project
Bibliographic record
Abstract
BACKGROUND: Adolescents and young adults with cancer (AYA) are a complex group of patients. The development of fever and neutropenia (FN) is a potentially lethal complication of chemotherapy. Risk stratification of patients with FN has become increasingly valuable allowing for early intervention and to guide treatment type and duration appropriately. There are risk stratification guidelines that exist, but most are validated in young children with cancer (YCWC). AYA are frequently shown to have more numerous and severe side effects from chemotherapy. AIMS: This study aimed to identify whether age contributes to the incidence and severity of FN. METHODS AND RESULTS: Patients diagnosed with a malignancy in a 5-year period at our institution were included from ages 0-18 years. We reviewed details of their FN events, including duration of hospital admission, source (bacterial/fungal), PICU admission and duration, positive blood cultures and mortality. Adolescents with cancer (AWC) had a trend of being 1.56 times more likely to have FN events (CI 95% 0.936-2.622, p = 0.087). Assessment of the duration of PICU stay showed that AWC were 4.9 times more likely to have longer admissions (CI 95% 0.998-24.067, p = 0.050). There was no significant difference between the two groups in the rate of PICU admission, positive cultures, identification of a bacterial or fungal source, hospital admission duration or mortality from FN. CONCLUSION: This study demonstrated a trend towards AWC being more likely to develop FN events. When such events occur in this group, the severity of them may be heightened as evidenced by longer duration of PICU admission.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".