Nonlesions, Misdiagnoses, Missed Diagnoses, and Other Interpretive Challenges in Fish Histopathology Studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Differentiating salient histopathologic changes from normal anatomic features or tissue artifacts can be decidedly challenging, especially for the novice fish pathologist. As a consequence, findings of questionable accuracy may be reported inadvertently, and the potential negative impacts of publishing inaccurate histopathologic interpretations are not always fully appreciated. The objectives of this article are to illustrate a number of specific morphologic findings in commonly examined fish tissues (e.g., gills, liver, kidney, and gonads) that are frequently either misdiagnosed or underdiagnosed, and to address related issues involving the interpretation of histopathologic data. To enhance the utility of this article as a guide, photomicrographs of normal and abnormal specimens are presented. General recommendations for generating and publishing results from histopathology studies are additionally provided. It is hoped that the furnished information will be a useful resource for manuscript generation, by helping authors, reviewers, and readers to critically assess fish histopathologic data.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it