Triage and Protocol Recommendations for the Parasitology Laboratory Based on an Epidemiological Investigation of Parasite Diagnostics in Ontario Laboratories
Bibliographic record
Abstract
OBJECTIVES: A study was performed using a subset of Ontario laboratory parasitology data, with three objectives: to describe parasitic infections in Ontario; to identify risk factors for acquiring a parasitic infection using routinely collected information; and to use this information to assess current protocols for parasite testing in laboratories and, in turn, to propose alternatives to optimize the allocation of laboratory resources. METHODS: All parasitology records from January 4, 2010 to September 14, 2010 were reviewed descriptively and risk factor analyses were performed using information collected from requisitions. These results were used to develop preliminary alternative protocols, which considered high-throughput screening tests and inclusion/exclusion criteria for ova and parasite testing; these were then retrospectively analyzed with the dataset to determine appropriateness. RESULTS: Of the 29,260 records analyzed, 10% were multiple samples from single patients submitted on the same day, of which 98% had the same result. Three percent of all parasite tests were positive, with the most prevalent parasites being (in ascending order) Dientamoeba fragilis, Giardia lamblia, Cryptosporidium species and Entamoeba histolytica/dispar. Age and sex were found to be weak risk factors, while rural living was found to be a moderate risk factor for D fragilis, G lamblia and Cryptosporidium infections. The strongest risk factor was travel history, especially for nonendemic parasites. The retrospective analysis of six alternative protocols identified four that may be more efficient than current procedures. CONCLUSIONS: The present study demonstrated that current protocols may be redundant and can be optimized to target prevalent parasites and populations with high risk factors.
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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 | 0.061 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".