Impact of molecular diagnostic techniques on the acute respiratory infection sentinel surveillance program, Antioquia, Colombia, 2022
Bibliographic record
Abstract
Objectives Surveillance of acute respiratory infection (ARI) informs vaccination, preventive, and management decisions. In many countries, immunofluorescence is the cornerstone for ARI surveillance. We aimed to determine the effect of adding multiplex polymerase chain reaction (mPCR) to conventional surveillance in ARI. Methods Respiratory samples from patients with influenza-like illness (ILI) and severe acute respiratory infection (SARI) were tested by a conventional approach [direct immunofluorescence (DIF) and SARS-CoV-2 PCR, and a subset of samples underwent routine testing]. Negative specimens were tested by multiplex PCR (mPCR), and remain negative samples were sequenced. Descriptive, multivariable regression analyses were conducted. Results Between March and June 2022, 299 patients were enrolled. Pathogens were detected in 43.8% of samples (131/299) tested by the conventional approach. Of the 168 negatives after the conventional approach, 157 (93.4%) were positive by mPCR, increasing the detection rate to 96.3% (288/299). With the conventional approach, the most frequent pathogen was respiratory syncytial virus (50.3%, 66/131), whereas with mPCR it was Haemophilus influenzae (37.5%, 63/168). mPCR significantly improved pathogen detection in ARI surveillance (Adjusted Incidence Rate Ratios 4.22 95% IC 4.22–5.85). Conclusion Adding mPCR to respiratory surveillance conventionally based on DIF significantly enhanced virus and bacteria detection. mPCR should be considered for routine ARI 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".