Postoperative Fevers in Pediatric Solid Tumor Patients
Bibliographic record
Abstract
UNLABELLED: It is unclear how aggressively postoperative fevers should be managed in immunosuppressed pediatric oncology patients after major surgery. Little data exists on this subject. Therefore, a retrospective study of patients treated at our center was undertaken to examine this. PURPOSES: (1) to describe the prevalence of fever and infection in postoperative pediatric solid tumor patients undergoing primary tumor resection, (2) to examine the risk factors that contribute to the development of postoperative infections, and (3) to describe the variation in practice in managing fevers. METHODS: Chart reviews were performed on patients diagnosed with a spectrum of tumor types from January 2000 to October 2005 who received preoperative chemotherapy, followed by tumor resection. RESULTS: Ninety-eight children met inclusion criteria and 73 (74%) developed fevers postoperatively; 14% of these had documented infections and 1 patient died from sepsis. Factors associated with increased risk of infection were a diagnosis of neuroblastoma (P=0.015), and surgery longer than 8 hours (P=0.059). The investigation and management of postoperative fevers varied in these patients. CONCLUSIONS: Postoperative fevers may be indicative of severe infection. We suggest that a standardized approach to the management of these patients, including prompt physical assessment, clinical investigations, and empiric antibiotic consideration is vital to minimize complications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".