Ultrastructural cardiac pathology: the wide (yet so very small) world of cardiac electron microscopy
Bibliographic record
Abstract
Electron microscopy (EM) was a popular diagnostic tool in the 1970s and early 80s. With the adoption of newer, less expensive techniques, such as immunohistochemistry, the role of EM in diagnostic surgical pathology has dwindled substantially. Nowadays, even in academic centers, EM interpretation is relegated to renal pathologists and the handful of (aging) pathologists with experience using the technique. As such, EM interpretation is truly arcane-understood by few and mysterious to many. Nevertheless, there remain situations in which EM is the best or only ancillary test to ascertain a specific diagnosis. Thus, there remains a critical need for the younger generation of surgical pathologists to learn EM interpretation. Recognizing this need, cardiac EM was made the theme of the Cardiovascular Evening Specialty Conference at the 2023 United States and Canadian Academy of Pathology (USCAP) annual meeting in New Orleans, Louisiana. Each of the speakers contributed their part to this article, the purpose of which is to review EM as it pertains to myocardial tissue and provide illustrative examples of the spectrum of ultrastructural cardiac pathology seen in storage/metabolic diseases, cardiomyopathies, infiltrative disorders, and cardiotoxicities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".