Comparison of Blood Smear, Antigen Detection, and Nested-PCR Methods for Screening Refugees from Regions Where Malaria Is Endemic after a Malaria Outbreak in Quebec, Canada
Bibliographic record
Abstract
The importation of malaria into a region where it is not endemic raises many concerns, including the timely delivery of appropriate care, safety of the blood supply, and the risk of autochthonous transmission. There is presently no consensus on the best way to screen mobile populations for malaria. Between August 2000 and March 2001, 535 refugees arrived in Quebec, Canada, from Tanzanian camps. Within 4 weeks of resettlement of the first group of 224, the McGill University Centre for Tropical Diseases noted an outbreak of malaria across the province (15 cases over a 3-week period). This group (group 1) was traced and screened for malaria between 3 and 4 months after arrival in Canada. Subsequent groups of 106 and 205 refugees were screened immediately upon arrival in Canada (group 2) and immediately prior to their departure from refugee camps (group 3), respectively. A single EDTA-blood sample was obtained from 521 refugees for testing by thick and thin blood smears (groups 1 and 2), antigen detection (ICT Malaria Pf and OptiMAL; group 1 only), and nested PCR (all groups). Overall, 98 of 521 refugees were found to be infected (18.8%). The vast majority of infections (81 of 98) were caused by Plasmodium falciparum alone. Using PCR as the "gold standard," both microscopy (sensitivity, 50%; specificity, 100%) and antigen detection (ICT sensitivity, 37.5%; ICT specificity, 100%; OptiMAL sensitivity, 29.1%; OptiMAL specificity, 95.6%) performed poorly. None of the PCR-positive subjects were symptomatic at the time of testing, and only two had recently had symptoms compatible with malaria (with or without diagnosis and treatment). Active surveillance of migrants from regions of intense malaria transmission can reduce the risk of morbidity in the migrant population and mitigate against transmission to the host population. Our data demonstrate that PCR is, by far, the most powerful tool for such 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".