Adult Patient Care Plan: Management of the Febrile Neutropenic Cancer Patient on an Outpatient Basis
Bibliographic record
Abstract
Invasive infection may complicate the course of neutropenic cancer patients receiving intensive chemotherapy. The rate of complications is related to prognostic factors including the underlying malignant diagnosis, the state of responsiveness of the underlying disease to treatment, the dose‐intensity of the cytotoxic therapy, the duration of neutropenia, the performance status of the patient and comorbid conditions. The pathogens involved are usually the patients’ endogenous microflora, and the sites of infection are those anatomic sites colonized with the endogenous microflora. The approach to the febrile neutropenic episode requires a sequence of steps including the recognition of the febrile state (oral temperature higher than 38°C), the depth and duration of the neutropenia (absolute neutrophil count less than 0.5×109/L), the identification of a clinical focus of infection and a potential pathogen, the administration of empirical antibacterial therapy, and finally, an assessment of the outcome. Management decisions about whether to treat with oral or parenteral antibacterial agents, with a combination or single agent therapy, or as an inpatient or an outpatient can be based on an assessments of risks of the severity of the patient’s comorbid conditions and the patient’s risk of developing medical complications that would require inpatient management. The duration of antimicrobial treatment depends on the recovery from the state of neutropenia and the origin of the infectious process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.006 |
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".