Statistical estimates of respiratory admissions attributable to seasonal and pandemic influenza for
Bibliographic record
Abstract
Background The number of admissions to hospital for which influenza is laboratory confirmed is considered to be a substantial underestimate of the true number of admissions due to an influenza infection. During the 2009 pandemic, testing for influenza in hospitalized patients was a priority, but the ascertainment rate remains uncertain. Methods The discharge abstracts of persons admitted with any respiratory condition were extracted from the Canadian Discharge Abstract Database, for April 2003–March 2010. Stratified, weekly admissions were modeled as a function of viral activity, seasonality, and trend using Poisson regression models. Results An estimated 1 out of every 6Æ4 admissions attributable to seasonal influenza (2003–April 2009) were coded to J10 (influenza virus identified). During the 2009 pandemic (May– March 2010), the influenza virus was identified in 1 of 1Æ6 admissions (95 % CI, 1Æ5–1Æ7) attributed to the pandemic strain. Compared with previous H1N1 seasons (2007 ⁄ 08, 2008 ⁄ 09), the influenza-attributed hospitalization rate for persons <65 years was approximately six times higher during the 2009 H1N1 pandemic, whereas for persons 75 years or older, the pandemic rate was approximately fivefold lower. Conclusions Case ascertainment was much improved during the pandemic period, with under ascertainment of admissions due to H1N1 ⁄ 2009 limited primarily to patients with a diagnosis of pneumonia.
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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 | 0.019 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".