Sensitivity and specificity of histology for diagnoses of four common pathogens and detection of nontarget pathogens in adult Chinook salmon ( <i>Oncorhynchus tshawytscha</i> ) in fresh water
Bibliographic record
Abstract
Histology is often underutilized in aquatic animal disease screening and diagnostics. The agreement between histological classifications of infection and results using diagnostic testing from the American Fisheries Society's Blue Book was conducted with 4 common salmon pathogens: Aeromonas salmonicida, Renibacterium salmoninarum, Ceratomyxa shasta, and Nanophyetus salmincola. Adult Chinook salmon (Oncorhynchus tshawytscha) in Oregon were evaluated, and agreement between tests was calculated. Live and dead (both pre- and postspawning) salmon were collected from the Willamette River, Oregon, its tributaries, the Willamette Hatchery, and after holding in cool, pathogen-free water during maturation at Oregon State University. Sensitivity and specificity of histology compared to Blue Book methods for all fish, live fish only, and dead (pre- and postspawned combined) fish only were, respectively, as follows: A. salmonicida (n = 105): specificity 87.5%, 87.5%, 87.5% and sensitivity 38.6%, 14.8%, 60.0%; R. salmoninarum (n = 111): specificity 91.9%, 85.7%, 97.7% and sensitivity 16.0%, 7.1%, 27.2%; C. shasta (n = 136): specificity 56.0%, 63.3%, 28.6% and sensitivity 83.3%, 86.2%, 71.4%; N. salmincola (n = 228): specificity 68.2%, 66.7%, not possible to calculate for dead fish and sensitivity 83.5%, 80.5%, 87.3%. The specificity was good for bacterial pathogens. This was not the case for C. shasta, likely due to detection of presporogenic forms only by histology. Sensitivity of histology for bacterial pathogens was low with the exception of dead fish with A. salmonicida. Kappa analysis for agreement between Blue Book and histology methods was poor to moderate. However, histological observations revealed the presence of other pathogens that would not be detected by other methods.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".