Hospital Triage System for Adult Patients Using an Influenza-Like Illness Scoring System during the 2009 Pandemic—Mexico
Bibliographic record
Abstract
BACKGROUND: Pandemic influenza A (H1N1) virus emerged during 2009. To help clinicians triage adults with acute respiratory illness, a scoring system for influenza-like illness (ILI) was implemented at Hospital Civil de Guadalajara, Mexico. METHODS: A medical history, laboratory and radiology results were collected on emergency room (ER) patients with acute respiratory illness to calculate an ILI-score. Patients were evaluated for admission by their ILI-score and clinicians' assessment of risk for developing complications. Nasal and throat swabs were collected from intermediate and high-risk patients for influenza testing by RT-PCR. The disposition and ILI-score of those oseltamivir-treated versus untreated, clinical characteristics of 2009 pandemic influenza A (H1N1) patients versus test-negative patients were compared by Pearson's Chi(2), Fisher's Exact, and Wilcoxon rank-sum tests. RESULTS: Of 1840 ER patients, 230 were initially hospitalized (mean ILI-score = 15), and the rest were discharged, including 286 ambulatory patients given oseltamivir (median ILI-score = 11), and 1324 untreated (median ILI-score = 5). Fourteen (1%) untreated patients returned, and 3 were hospitalized on oseltamivir (median ILI-score = 19). Of 371 patients tested by RT-PCR, 104 (28%) had pandemic influenza and 42 (11%) had seasonal influenza A detected. Twenty (91%) of 22 imaged hospitalized pandemic influenza patients had bilateral infiltrates compared to 23 (38%) of 61 imaged hospital test-negative patients (p<0.001). One patient with confirmed pandemic influenza presented 6 days after symptom onset, required mechanical ventilation, and died. CONCLUSIONS: The triaging system that used an ILI-score complimented clinicians' judgment of who needed oseltamivir and inpatient care and helped hospital staff manage a surge in demand for services.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".