Peer Review #1 of "Characterization of viral pathogens associated with symptomatic upper respiratory tract infection in adults during a low COVID-19 transmission period (v0.2)"
Bibliographic record
Abstract
Background: The epidemiology of respiratory tract infections (RTI) has dramatically changed over the course of the COVID-19 pandemic.A major effort in the clinical management of RTI has been directed toward diagnosing COVID-19, while the causes of other, common community RTI often remain enigmatic.To shed light on the etiological causes of RTI during a low COVID-19 transmission period in 2021, we did a pilot study using molecular testing for virologic causes of upper RTI among adults with respiratory symptoms from Almaty, Kazakhstan.Methods: Adults presenting at two public hospitals with respiratory symptoms were screened using SARS-CoV-2 PCR on nasopharyngeal swabs.A subset of RTI+, COVID-19-negative adults (n=50) was then tested for the presence of common RTI viruses and influenza A virus (IAV).Next generation virome sequencing was used to further characterize the PCR-detected RTI pathogens.Results: Of 1812 symptomatic adults, 21 (1.2%) tested SARS-CoV-2-positive.Within the COVID-19 negative outpatient subset, 33/50 subjects (66%) had a positive PCR result for a common community RTI virus, consisting of human parainfluenza virus 3-4 (hPIV 3-4) in 25/50 (50%), rhinovirus (hRV) in 2 (4%), hPIV4-hRV co-infection in 4 (8%) and adenovirus or the OCR43/HKU-1 coronavirus in 2 (4%) cases; no IAV was detected.Virome sequencing allowed to reconstruct sequences of most PCR-identified rhinoviruses and hPIV-3/human respirovirus-3.Conclusions: COVID-19 was cause to a low proportion of symptomatic RTI among adults.Among COVID-negative participants, symptomatic RTI was predominantly associated with hPIV and hRV.Therefore, respiratory viruses other than SARS-CoV-2 should be considered in the clinical management and prevention of adult RTI in the post-
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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 | 0.011 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.374 | 0.201 |
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".