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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".