Practical Strategies to Improve the Clinical Utility of the Dermatopathology Report
Bibliographic record
Abstract
CONTEXT: -Dermatologists and subspecialty dermatopathologists, working together over many years, develop a common understanding of clinical information provided on the requisition and of terminology used in the pathology report. Challenges arise for pathologists without additional subspecialty training in dermatology/dermatopathology, and for any pathologist reporting skin biopsies for nondermatologists such as general practitioners or surgeons. OBJECTIVE: -To provide practical strategies to improve efficiency of dermatopathology sign-out, at the same time providing the clinician with clear diagnostic and prognostic information to guide patient management. DATA SOURCES: -The information outlined in this review is based on our own experiences with routine dermatopathology and dermatology practice, and review of English-language articles related to the selected topics discussed. CONCLUSIONS: -Using generic diagnoses for some benign lesions, listing pertinent negatives in the pathology report, and using logical risk management strategies when reporting on basal cell carcinoma, partial biopsies, or specimens with incomplete clinical information allow the pathologist to convey relevant and useful diagnostic information to the treating clinician.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".