Detection of Pathogenic Protozoa in the Diagnostic Laboratory: Result Reproducibility, Specimen Pooling, and Competency Assessment
Bibliographic record
Abstract
Stool microscopy as performed in clinical parasitology laboratories is a complex procedure with subjective interpretation. Quality assurance (QA) programs often emphasize proficiency testing as an assessment tool. We describe a result reproducibility assessment tool, which can form part of a broader QA program, and which is based on the blinded resubmission of selected clinical samples, using concordance between the reports of the initial and resubmitted specimen as an indicator. Specimens preserved in sodium acetate-acetic acid-formalin can be stored for several months for use in such a program. The presence of multiple protozoa in one specimen does not affect concordance. Some dilution of specimens occurs in this process, and this may explain poor concordance when specimens with low protozoal concentrations are resubmitted. Evaluation of this tool in a large parasitology laboratory revealed concordance rates for pathogenic protozoa (Entamoeba histolytica/Entamoeba dispar, Giardia lamblia, and Dientamoeba fragilis) of about 80%, which may be considered for use as a benchmark value. We also used this tool to demonstrate that when pairs of specimens from one patient are pooled to create a single specimen, concordance between the results of the individual and pooled specimens is high.
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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 | 0.093 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".