Prospective Step Sections for Small Skin Biopsies
Bibliographic record
Abstract
Abstract Context. —In our laboratory, for small skin biopsies or curetted specimens, 3 slides are prepared before the case is reviewed by the dermatopathologist. Objective. —To examine the utility of these “prospective” step sections in improving diagnostic accuracy and turnaround time. Design. —Five hundred consecutive cases, in which step sections had been cut prior to slide review, were studied. For each specimen, 3 slides, each consisting of 1 ribbon of tissue containing 4 to 6 sections, were obtained at 50-μm intervals from the paraffin block. Results. —Fifty-eight biopsies (12%) were nondiagnostic using slide 1 alone. Step sections provided a diagnosis in 19 of 58 cases. In an additional 15 cases (3%) in which a diagnosis was possible using slide 1, deeper levels resulted in a change in diagnosis. Thus, in 34 (7%) of 500 biopsies, deeper levels resulted in improved diagnostic accuracy. In addition, the pathologist would have ordered step sections in a further 117 cases (23%) to clarify the diagnosis rendered on level 1 or to exclude other lesions. Thus, 30% of small skin biopsies would have required deeper levels if step sections had not been obtained prior to slide review. Conclusions. —In our laboratory, the use of prospective step sections is essentially cost-neutral and case turnaround time is improved by 9% to 45%. Step sections result in a changed diagnosis in 7% of small skin biopsy specimens.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".