Delayed Diagnosis of Respiratory Syncytial Virus Infections in Hospitalized Adults: Individual Patient Data, Record Review Analysis and Physician Survey in the United States
Bibliographic record
Abstract
BACKGROUND: Despite the prevalence of respiratory syncytial virus (RSV) in adults hospitalized with acute respiratory infections, guidelines for the diagnosis and management of RSV have not been established. This analysis evaluated the role and timeliness of RSV diagnostic testing and its potential impact on clinical outcomes. METHODS: We analyzed individual patient data from hospitalized adults with confirmed RSV infections during 2 North American RSV seasons. Participating physicians reported clinical, virologic diagnosis, and outcome variables using a standardized online case form. RESULTS: Across 32 US states, 132 physicians reported 379 RSV cases. Polymerase chain reaction-based diagnostics were the most common type of test ordered (94.2%) with <5% ordered specifically to diagnose RSV. Most tests (67.6%) were ordered in hospital wards or intensive care units. Overall, 47.4%, 30.9%, and 21.7% of patients had RSV diagnosed <12, 12‒24, and >24 hours after hospital admission, respectively. Later diagnosis was associated with longer hospital stays (n = 145; R = +0.191; P < .05) and greater antibiotic use. CONCLUSION: Diagnosis of RSV infection in hospitalized adults is often delayed, which may affect clinical management and outcomes. Our findings indicate the need to improve the diagnostic strategies in this patient population.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".