Low prevalence of complications in severe neutropenic children with cancer in the unprotected environment of an overnight camp
Bibliographic record
Abstract
BACKGROUND: The high risk of infection and other complications in severely neutropenic pediatric oncology patients receiving chemotherapy has led to development of a variety of preventive measures including isolation and diet restrictions. In order to examine the potential impact of these measures, we evaluated the outcomes of such patients attending a recreational summer camp. METHODS: We collected data on all children who attended an overnight summer camp for children with cancer during the years 1999-2004, and who were either severely neutropenic or at a high-intensity phase of chemotherapy. Outcome measures included fever, bleeding, hospitalization, and clinical or laboratory evidence of infection. The observation period included both, the 2-week camp experience and 30 days post-camp. RESULTS: The study group was comprised of 34 patients. Although nine (24%) were hospitalized for management of fever and neutropenia, only one patient had clinical or culture-positive evidence of an invasive infectious agent. No bleeding episode was recorded and most patients attended all camp activities. CONCLUSIONS: Our results support the safety and feasibility of severely neutropenic patients with cancer to attend the non-isolated, non-sterile environment of a summer camp. These findings may be applicable to school and other social settings.
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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 | 0.000 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".