The accuracy and timeliness of neuraminidase inhibitor dispensing data for predicting laboratory-confirmed influenza
Bibliographic record
Abstract
Neuraminidase inhibitor (NI) dispensing has emerged as a possible automated data source for influenza surveillance. We aimed to evaluate its timeliness, correlation, and predictive accuracy in relation to influenza activity in Quebec, Canada, 2010-2013. Our secondary objective was to use the same metrics to compare NI dispensing to visits for influenza-like illness (ILI) in emergency departments (EDs). Provincial weekly counts of positive influenza laboratory tests were used as a reference measure for the level of influenza circulation. We applied ARIMA models to account for serial correlation. We computed cross-correlations to measure the strengths of association and lead-lag relationships between NI dispensing, ILI ED visits, and our reference indicator. Finally, using an ARIMA model, we evaluated the ability of NI dispensing and ILI ED visits to predict laboratory-confirmed influenza. NI dispensing was significantly correlated (R = 0·68) with influenza activity with no lag. The maximal correlation of ILI ED visits was not as strong (R = 0·50). Both NI dispensing and ILI ED visits were significant predictors of laboratory-confirmed influenza in a multivariable model; predictive potential was greatest when NI counts were lagged to precede laboratory surveillance by 2 weeks. We conclude that NI dispensing data provides timely and valuable information for influenza surveillance.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".