Validation of in situ hybridisation and histology assays for the detection of the oyster parasite Marteilia refringens
Bibliographic record
Abstract
An in situ hybridisation technique has been developed for the detection of infection in oysters with Marteilia refringens with particular emphasis on light infections or confirmation of suspected cases by means of histology. Although validation of new diagnostic methods is usually achieved by comparison with standard techniques, in our case the sensitivity and specificity of the standard (histology) had not previously been established. Another point to consider is that surveillance and monitoring frequently target populations displaying different levels of prevalence under different field conditions. The objective of our study was to evaluate the sensitivity and specificity values of in situ hybridisation and histology for the detection of M. refringens, based on 3 populations of flat oysters, free of the disease and with mild and high levels of prevalence. A blind assay of 200 individuals from each population was performed using both techniques. Results were analysed by means of the classical approach and latent models (maximum likehood and Bayesian approach). Assumptions and results were found to vary slightly with the different statistical approaches. The more realistic estimate by the Bayesian approach shows a link between the level of prevalence and the sensitivity of the techniques. Values of sensitivity and specificity for histology were 0.7 and 0.99 respectively, and 0.9 and 0.99 respectively in the case of in situ hybridisation. Some uncertainty remains regarding these values because the study does not take into account the severity of infection or the developmental stages of the parasite actually present in each individual. This work provides valuable information with regard to the choice and potential use of those 2 diagnostic methods currently recommended by international standards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".