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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".