Use of an Innovative Web-Based Laboratory Surveillance Platform to Analyze Mixed Infections Between Human Metapneumovirus (hMPV) and Other Respiratory Viruses Circulating in Alberta (AB), Canada (2009–2012)
Bibliographic record
Abstract
We investigated the proportions of mono vs. mixed infections for human metapneumovirus (hMPV) as compared to adenovirus (ADV), four types of coronavirus (CRV), parainfluenza virus (PIV), RSV, and enterovirus/rhinovirus (ERV) in Alberta, Canada. Using the Data Integration for Alberta Laboratories (DIAL) platform, 26,226 respiratory specimens at ProvLab between 1 July 2009 and 30 June 2012 were selected and included in the study. Using the Respiratory Virus Panel these specimens tested positive for one or more respiratory virus and negative for influenza A and B. From our subset hMPV was the fourth most common virus (n=2,561) with 373 (15%) identified as mixed infection using DIAL. Mixed infection with hMPV was most commonly found in infants less than 6 months old and ERV was most commonly found in mixed infection with hMPV (230/373, 56%) across all age groups. The proportion of mixed-infection vs. mono-infection was highest for ADV (46%), followed by CRV 229E (32%), CRV HKU1 (31%), CRV NL63 (28%), CRV OC43 (23%), PIV (20%), RSV (17%), hMPV (15%) and ERV (13%). hMPV was significantly more likely to be identified in mono infection as compared with ADV, CRV, PIV, and RSV with the exception of ERV [p < 0.05].
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".