Is Anti–h-Caldesmon Useful for Distinguishing Smooth Muscle and Myofibroblastic Tumors?
Bibliographic record
Abstract
Misinterpretation of positive staining of antibodies to desmin, smooth muscle actin, and muscle actin as representing smooth muscle differentiation in the context of a spindle cell tumor is not uncommon. Anti-h-caldesmon is a promising novel immunohistochemical reagent for more specific smooth muscle differentiation. We studied 72 tumors (11 leiomyosarcomas, 26 malignant fibrous histiocytomas [MFHs], 11 fibromatoses, 11 cellular cutaneous fibrous histiocytomas [CCFHs], 5 malignant peripheral nerve sheath tumors, 4 synovial sarcomas, and 4 cases of nodular fasciitis), the reactive myofibroblastic response in 5 cases of acute cholecystitis, and the desmoplastic response surrounding 5 invasive breast carcinomas. Tissues were examined for expression of h-caldesmon, desmin, smooth muscle actin, and muscle actin. Diffuse staining for h-caldesmon was present only within the leiomyosarcomas. Focal staining for h-caldesmon involving less than 1% of lesional cells was present in 3 of 26 MFHs and 1 of 11 CCFHs. There was overlap in staining for the other "myoid" markers in all of the lesions that contained myofibroblasts. Anti-h-caldesmon seems to be a reliable marker of smooth muscle differentiation, and its inclusion in a panel of myoid immunohistochemical reagents should allow distinction of smooth muscle and myofibroblastic tumors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".