Assessing the reporting quality of influenza outbreaks in the community
Bibliographic record
Abstract
BACKGROUND: High-quality reporting of outbreak characteristics is fundamental to understand the behaviour of various strains of influenza virus and the impact of outbreak management strategies. However, few studies have systematically evaluated the quality of outbreak reporting. OBJECTIVES: To conduct a systematic analysis and assessment for reporting quality of influenza outbreaks based on a modified version of the STROBE statement, and to examine characteristics associated with reporting quality. METHODS: A literature search was conducted across 3 online databases (PubMed, Web of Science, MEDLINE) for reports of influenza outbreaks (pandemic H1N1, avian, seasonal). The quality of reports meeting our eligibility criteria was assessed using the Modified STROBE criteria and assigned a score of 30. Mean differences (MD) and 95% confidence intervals (CI) were reported for comparisons of study characteristics. RESULTS: Sixty-four outbreak reports were available for analyses. The average Modified STROBE score was 20/30. Peer-reviewed articles were associated with a better quality of reporting (MD 2.79, 95% CI 0.79-4.78). Likewise, reports from authors affiliated with public health agencies were associated with better quality than those from academic institutions (MD 1.65, 95% CI-0.27-3.56). CONCLUSIONS: The development of explicit reporting guidelines specifically geared towards reporting of outbreak investigations proved to be useful. Providing information on patient characteristics, investigation details in introduction and results, as well as addressing limitations that could have biased the findings, were frequently missing in the published reports.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.414 | 0.723 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".