Using quantitative tissue phenotype to assess the margins of surgical samples from a pan‐Canadian surgery study
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to use quantitative tissue phenotype (QTP) to assess the surgical margins to examine if a fluorescence visualization-guided surgical approach produces a shift in the surgical field by sparing normal tissue while catching high-risk tissue. METHODS: Using our QTP to calculate the degree of nuclear chromatin abnormalities, Nuclear Phenotypic Score (NPS), we analyzed 1290 biopsy specimens taken from surgical samples of 248 patients enrolled in the Efficacy of Optically-guided Surgery in the Management of Early-staged Oral Cancer (COOLS) trial. Multiple margin specimens were collected from each surgical specimen according to the presence of fluorescence visualization alterations and the distance to the surgical margins. RESULTS: The NPS in fluorescence visualization-altered (fluorescence visualization-positive) samples was significantly higher than that in fluorescence visualization-retained (fluorescence visualization-negative) samples. There was a constant trend of decreasing NPS of margin samples from non-adjacent-fluorescence visualization margins to adjacent-fluorescence visualization margins. CONCLUSION: Our results suggested that using fluorescence visualization to guide surgery has the potential to spare more normal tissue at surgical margins.
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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 | 0.000 | 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 | 0.000 |
| 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".