Utilization and Utility of Immunohistochemistry in Dermatopathology
Bibliographic record
Abstract
Immunohistochemistry (IHC) is considered a valuable ancillary tool for dermatopathology diagnosis, but few studies have measured IHC utilization by dermatopathologists or assessed its diagnostic utility. In a regionalized, community-based dermatopathology practice, we measured IHC utilization (total requests, specific antibodies requested, and final diagnosis) over a 12-month period. Next, we assessed diagnostic utility by comparing a preliminary "pre-IHC" diagnosis based on routine histochemical staining with the final diagnosis rendered after consideration of IHC results. The dermatopathology IHC utilization rate was 1.2%, averaging 3.6 stains requested per case. Melanocytic, hematolymphoid, and fibrohistiocytic lesions made up 23%, 18%, and 16%, respectively, of the total cases requiring IHC. S100 and Melan A were the most frequently requested stains, ordered on 50% and 34% of IHC cases, respectively. The utility study revealed that IHC changed the diagnosis in 11%, confirmed a diagnosis, or excluded a differential diagnosis in 77%, and was noncontributory in 4% of cases. Where IHC results prompted a change in diagnosis, 14% were a change from a benign to malignant lesion, whereas 32% changed from one malignant entity to another. IHC is most commonly used in cutaneous melanocytic and hematolymphoid lesions. In 11% of dermatopathology cases in which IHC is used, information is provided that changes the H&E diagnosis. Such changes may have significant treatment implications. IHC is noncontributory in only a small percentage of cases.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".