Impact of Routine Real-Time PCR Testing of Imported Malaria over 4 Years of Implementation in a Clinical Laboratory
Bibliographic record
Abstract
In clinical laboratories, diagnosis of imported malaria is commonly performed by microscopy. However, the volume of specimens is generally low and maintaining proficiency in reading blood smears, particularly at the species level, is challenging in this setting. To address this problem, the Provincial Laboratory for Public Health (ProvLab) in Alberta, Canada, implemented real-time PCR for routine confirmation of all smear-positive samples in the province. Here we report our experience over a 4-year period (2008 to 2012) with this new diagnostic algorithm. While detection of Plasmodium falciparum by microscopy alone was accurate, real-time PCR served as an important adjunct to microscopy for the identification of non-falciparum species. In 18% of cases, the result was reported as non-falciparum or the species could not be identified by microscopy alone, and in all cases, the species was resolved by real-time PCR. In another 4% of cases, the species was misidentified by microscopy. To enhance surveillance for malaria, we integrated our demographic, clinical, and laboratory data into a new system developed by the Canadian Network for Public Health Intelligence, called the Malaria System for Online Surveillance (SOS). Using this application, we characterized our patient populations and travel history to identify risk factors associated with malaria infection abroad.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".