Laboratory methods for case finding in human psittacosis outbreaks: a systematic review
Bibliographic record
Abstract
BACKGROUND: Psittacosis outbreak investigations require rapid identification of cases in order to trace possible sources and perform public health risk assessments. In recent outbreaks in the Netherlands, such investigations were hampered by the non-specificity of laboratory testing methods to identify human Chlamydia psittaci infections. METHOD: A systematic search of PubMed and Scopus databases of literature published between 01 January, 1986 and 03 July, 2017 was done to find best practices of laboratory-testing methods used in psittacosis outbreaks of two or more human cases. Reference lists of included articles were hand searched to identify additional articles. RESULTS: Thirty-seven eligible articles were identified, describing 44 human psittacosis outbreaks in 12 countries. Laboratory tests performed were PCR (with various targets), serologic tests (complement binding reactions, ELISA's, immunofluorescence tests and immuno-peroxidase tests) and culture, in various combinations. The literature provided no 'gold standard' laboratory testing strategy to identify recent human C. psittaci infections. In most psittacosis outbreaks, for a considerable number of cases (or tested individuals in an exposed cohort), C. psittaci infection could not be confirmed, nor excluded as causative pathogen. None of the testing strategies was found to be suitable for (nearly) full case finding. CONCLUSION: PCR enables rapid identification of human psittacosis patients and helps source finding by genotyping but has the disadvantage that sensitivity is high only in the acute phase. In outbreak situations, there is often a time delay and therefore, there is a need for new serologic testing methods next to PCR, with good specificity and sensitivity. Moreover, serum is easier to collect than the preferred diagnostic materials for PCR. A serologic test that can reliably confirm infection status without the necessity of convalescent serum sampling would enhance case finding, source tracing, identification of risk factors and assessment of burden of disease in various settings.
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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 | 0.010 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".