Obtaining High Cure Rates for Challenging Facial Malignancies: A New Method for Producing Rapid, Accurate, High-Quality Frozen Sections
Bibliographic record
Abstract
PURPOSE: The authors developed a new system to provide rapid, accurate, full-face frozen sections. OBJECTIVE: To evaluate the efficacy of the system when applied to the treatment of nonmelanoma cutaneous malignancies using Mohs micrographic surgery (MMS). METHODS: Patients undergoing MMS procedures between 2003 and 2007 for nonmelanoma head and neck cutaneous malignancies were prospectively collected. Specimens were prepared either in a traditional cryostat-based manner or using the new system. RESULTS: A total of 196 patients with 234 head and neck nonmelanoma cutaneous malignancies were included. The majority of tumours were basal cell carcinomas (89.5%). Of these, 38% demonstrated aggressive histologies (sclerosing or micronodular), and 30% were recurrent. On average, two levels (range one to six) and four blocks (range two to 23) were required to obtain clear margins. The mean defect size was 3.68 cm(2) (range 0.13 cm(2) to 37.68 cm(2)). Over the five-year study period, there were two recurrences in 234 cases (less than 1%), which compares favourably with other MMS series. The new system was associated with a shorter operative time than traditional specimen preparation (102 min versus 131 min; P=0.004). The new and traditional specimen preparation groups were similar in terms of the number of previous recurrences (29% versus 30%; P=1.00), defect size (3.7 cm(2) versus 4.0 cm(2); P=0.81) and the number of levels required (1.9 versus 1.5; P=0.05). CONCLUSIONS: The new system enables fast, accurate, full-face frozen section specimens that are ideal for MMS. The speed of specimen preparation is demonstrated by faster operative times, and a low recurrence rate attests the accuracy and quality of the sections.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".