Assessing the diagnostic accuracy of PCR-based detection of <i>Streptococcus pneumoniae</i> from nasopharyngeal swabs collected for viral studies in Canadian adults hospitalised with community-acquired pneumonia: a Serious Outcomes Surveillance (SOS) Network of the Canadian Immunization Research (CIRN) study
Bibliographic record
Abstract
Study design Detection and serotyping of Streptococcus pneumoniae are important to assess the impact of pneumococcal vaccines. This study describes the diagnostic accuracy of PCR-based detection of S. pneumoniae directly from nasopharyngeal (NP) swabs collected for respiratory virus studies. Methods Active surveillance for community-acquired pneumonia (CAP) in hospitalised adults was performed from December 2010 to 2013. Detection of pneumococcal CAP (CAP Spn ) was performed by urine antigen detection (UAD), identification of S. pneumoniae in sputum or blood cultures. S. pneumoniae was detected in NP swabs using lytA and cpsA real-time PCR, and serotyping was performed using conventional and real-time multiplex PCRs. For serotyping, the Quellung reaction, PCR-based serotyping or a serotype-specific UAD was used. Results NP swab results were compared against CAP cases where all pneumococcal tests were performed (n=434), or where at least one test was performed (n=1616). CAP Spn was identified in 22.1% (96/434) and 14.9% (240/1616), respectively. The sensitivity of NP swab PCR for the detection of S. pneumoniae was poor for CAP Spn (35.4% (34/96) and 34.17% (82/240)), but high specificity was observed (99.4% (336/338) and 97.89% (1347/1376)). Of the positive NP swabs, a serotype could be deduced by PCR in 88.2% (30/34) and 93.9% (77/82), respectively. Conclusions While further optimisation may be needed to increase the sensitivity of PCR-based detection, its high specificity suggests there is a value for pneumococcal surveillance. With many laboratories archiving specimens for influenza virus surveillance, this specimen type could provide a non-culture-based method for pneumococcal 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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".