Evaluation of histopathology, PCR, and qPCR to detect Mikrocytos mackini in oysters Crassostrea gigas using Bayesian latent class analysis
Bibliographic record
Abstract
Latent class analysis (LCA) is a common method to evaluate the diagnostic sensitivity (DSe) and specificity (DSp) for pathogen detection assays in the absence of a perfect reference standard. Here we used LCA to evaluate the diagnostic accuracy of 3 tests for the detection of Mikrocytos mackini in Pacific oysters Crassostrea gigas: conventional polymerase chain reaction (PCR), real-time quantitative PCR (qPCR), and histopathology. A total of 802 Pacific oysters collected over 12 sampling events from 9 locations were assessed. Preliminary investigations indicated that standard LCA assumptions of test independence and constant detection accuracy across locations were likely unrealistic. This was mitigated by restructuring the LCA in a Bayesian framework to include test-derived knowledge about pathogen prevalence and load for categorizing populations into 2 classes of infection severity (low or high) and assessing separate DSe and DSp estimates for each class. Median DSp estimates were high (>96%) for all 3 tests in both population classes. DSe estimates varied between tests and population classes but were consistently highest for qPCR (87-99%) and lowest for histopathology (21-51%). Acknowledging that detection of M. mackini may be fitted to multiple diagnostic and management purposes, qPCR had the highest DSe while maintaining similar DSp to both conventional PCR and histopathology and thus is generally well-suited to most applications.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".