Evaluation of influenza case definitions for use in real-world evidence research
Bibliographic record
Abstract
Background: Laboratory confirmation of influenza is not routinely done in practice. With the advent of big data, it is tempting to use healthcare administrative databases for influenza vaccine effectiveness studies, which often rely on clinical diagnosis codes. The objective of this article is to compare influenza incidence curves using international case definitions derived from clinical diagnostic codes with influenza surveillance data from the United States (US) Centers for Disease Control and Prevention (CDC). Methods: This case series describes influenza incidence by CDC week, defined using International Classification of Disease diagnostic codes over four influenza seasons (2015-2016 to 2018-2019) in a cohort of US individuals three years of age and older who consulted at least once per year between 2015 and 2019. Results were compared to the number of influenza-positive specimens or outpatient visits for influenza-like illness obtained from the CDC flu surveillance data. Results: The incidence curves of influenza-related medical encounters were very similar to the CDC's surveillance data for laboratory-confirmed influenza. Conversely, the number of influenza-like illness encounters was high when influenza viruses started to circulate, leading to a discrepancy with CDC-reported data. Conclusion: A specific case definition should be prioritized when data for laboratory-confirmed influenza are not available, as a broader case definition would conservatively bias influenza vaccine effectiveness toward the null.
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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 | 0.504 | 0.804 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.033 | 0.025 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".