Contribution of Electron Microscopy to Understanding Cellular Differentiation in Mesenchymal Tumors of the Gastrointestinal Tract: A Study of 82 Tumors
Bibliographic record
Abstract
Eighty-two mesenchymal tumors of the gastrointestinal tract were examined by electron microscopy for the purposes of subtyping for diagnostic precision and of understanding cellular differentiation. Tumors were subclassified into leiomyoma/leiomyosarcoma, tumors of the interstitial cell of Cajal (equivalent to traditionally defined GISTs [Miettinen et al. Hum Pathol. 1999; 30:1213-1220; Mod Pathol. 2000; 13:1134-1142]), gastrointestinal autonomic nerve tumors (GANTs), and fibroblastic and myofibroblastic tumors, using criteria from the literature. Leiomyoma/leiomyosarcoma were diagnosed by myofilaments, attachment plaques, plasmalemmal caveolae, and lamina; GIST by processes or cell bodies full of intermediate filaments, solitary focal densities amid intermediate filaments, attachment plaques with incomplete lamina, scarce myofilaments, and smooth endoplasmic reticulum; GANTs by neuroendocrine granules, cell bodies/processes full of intermediate filaments (more rarely microtubules), and smooth endoplasmic reticulum; fibroblastic/myofibroblastic tumors by abundant rough endoplasmic reticulum, myofilaments, and fibronexuses. Seventy-three tumors (89%) were successfully subclassified, as 5 leiomyoma/leiomyosarcoma (6%), 36 GISTs (44%), 22 GANTs (27%), 10 fibroblastic and myofibroblastic tumors (12%). Results indicated overlap between poorly differentiated leiomyosarcoma and GIST, and between GIST and GANT. GANT is emphasized as a neuronal tumor identifiable by electron microscopy, and thereby distinguishable from GIST.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".