790Predictors of Influenza Infection in Older Adults Presenting to Emergency Departments (EDs) in Toronto, Canada
Bibliographic record
Abstract
Background. Diagnosis of influenza in the elderly may be complicated by atypical presentations. We compared the characteristics of community-dwelling adults aged ≥60 years presenting to EDs during the 2011/2012 and 2012/2013 influenza seasons. Methods. We identified patients presenting to six EDs with influenza-compatible symptoms. Clinical characteristics, medical history and demographics were collected by patient interview, chart review and from vaccine providers. NP swabs were tested for flu using PCR. We modeled predictors of flu using multivariable logistic regression models that compared cases to test negative controls. Results. Of 1318 participants, 151 (11%) had flu (98 AH3N2, 12 AH1N1, 4 A(not subtyped), 37 B). In multivariable models, factors associated with flu were cough (A: OR 4.00, 95%CI 1.67-9.60; B: OR 10.4, 95%CI 2.35-45.8), feverishness and/or T ≥ 37.20C (A: OR 3.81, 95%CI 2.22-6.54; B: 2.36, 95%CI 1.02-5.47), symptom duration of 2-5d (A: OR 2.15, 95%CI 1.35-3.43; B: OR 2.27, 95%CI 1.05-4.94) and level of flu in the community (A: OR 1.04, 95%CI 1.00-1.07; B: OR 1.13, 95%CI 1.08-1.19). The CDC ILI definition identified 47 (31%) flu cases. Additional factors associated with flu A included having any respiratory symptom (OR 2.29, 95%CI 1.22-4.23), working with children (OR 12.3, 95%CI 2.50-60.7), recent exposure to ILI (OR 1.74, 95%CI 1.08-2.83) and older age (OR 1.03, 95%CI 1.00-1.05). Confusion was associated with flu B among those not frail at baseline. As age increased, cough was more predictive of flu A and B. Characteristics of participants with no flu, flu A and flu B (n=1318) *p<0.05 when compared against no flu Conclusion. Cough and fever are as predictive of flu in the elderly as in younger adults but standard case definitions miss most patients. Adding epidemiological factors may be helpful for flu diagnoses. Disclosures. A. Mcgeer, GSK: Grant Investigator and Scientific Advisor, Research grant; Sanofi Pasteur: Grant Investigator and Scientific Advisor, Research support
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".