A Novel Mohs Precision Tool
Bibliographic record
Abstract
BACKGROUND: Effective treatment by Mohs micrographic surgery requires preparation of high-quality slides. OBJECTIVE: To examine a novel tissue alignment device designed to address variability in tissue processing because of excessive sample trimming. MATERIALS AND METHODS: A device was designed to account for angular errors and unparalleled tissue embedding. A retrospective chart review was performed both with and without the use of the device over the course of a 4-year period (2012-2015). RESULTS: Between January 1, 2012, and June 10, 2014, before device implementation, mean number of stages per case was 1.65 (n = 3,680) and mean number of surgeries per day was 6.34 (n = 640). Between June 11, 2014, and October 02, 2015, with device implemented, the average number of stages per case between decreased to 1.58 (n = 2,562) and the number of daily surgeries increased to 7.05 (n = 358). This represents a significant decrease in number of stages per case by 0.07 stages (95% CI: -0.01 to -0.13, p = .02), as well as an increase in the number of cases per day by 0.71 cases (95% CI: 0.12-1.3, p < .01). CONCLUSION: Slide preparation using the novel alignment device may result in less tissue waste and more cases being performed daily.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".