Diagnostic Testing or Empirical Therapy for Patients Hospitalized with Suspected Influenza: What to Do?
Bibliographic record
Abstract
Accumulating evidence supports the use of specific diagnostic tests and antiviral therapies for seriously ill patients with influenza. Among available diagnostic tests, reverse-transcriptase polymerase chain reaction is faster than culture and more sensitive than commercial antigen assays. Current neuraminidase inhibitors were approved on the basis of their efficacy in ambulatory patients, but seriously ill patients who receive these agents are less likely to die, even when treatment is initiated >48 h after symptom onset. For patients hospitalized with suspected influenza, it is unclear which circumstances warrant diagnostic testing and which warrant the use of empirical therapy. Rapid antigen assays may reduce the unnecessary use of other tests and medications but are relatively insensitive, thus eliminating many patients with influenza as candidates for treatment. Empirical antiviral therapy ensures that all patients receive treatment promptly, at a cost equivalent to that of diagnostic tests alone, but results in the receipt of treatment by many patients without influenza. For patients hospitalized with suspected influenza, clinicians need to combine these approaches in order to optimize patient care.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".