Bibliographic record
Abstract
We thank Chan-Tack and Murray [1] for their thoughtful comments about our study. They highlight a current dilemma for clinicians, who have to decide on whether to use antiviral therapy for hospitalized patients in the absence of definitive data, and they raise interesting issues about the selection of outcomes in studies of severe influenza. Randomized, controlled trials of outpatients demonstrate that antivirals are effective for improving outcomes in children and healthy adults infected with influenza virus [2, 3]. There are at least 5 cohort studies of high-risk patients (in addition to ours) that suggest significant benefits to therapy (mortality in 3 studies, length of hospital stay in 1, and progression to pneumonia in 1) [4–8]; in addition, numerous studies have demonstrated prolonged viral shedding in compromised hosts. Although it would be of obvious benefit to have data from randomized, controlled trials, it is not surprising that expert guidelines now recommend treatment for patients at high risk who have progressive disease due to laboratory-confirmed influenza, regardless of the time from onset of symptoms [9, 10]. The most appropriate end points for studies of severe influenza disease have yet to be defined. In part, this is because the epidemiology and clinical features of severe influenza in adults have not been carefully studied. Patients who require hospital admission for influenza may have primary influenza disease, disease secondary to bacterial infection, or exacerbations of underlying cardiac or respiratory illness. Mortality is the most important outcome and is the logical choice in the absence of other validated end points. However, trials will be smaller and information will be more useful if clinically important differences in outcomes in survivors can be identified. Validated outcome measures for clinical trials of severe influenza are needed. Potential conflicts of interest. A.J.M. and D.E.L.: no conflicts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.042 | 0.084 |
| Insufficient payload (model declined to judge) | 0.007 | 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".