OPTICAL PROPERTIES FOR LUNG CANCER MARGIN DETECTION
Bibliographic record
Abstract
Lung cancer is the leading cause of death from cancer in Canada and is typically treated with surgical resection of the tumor. To ensure good prognosis and limit metastases no cancer cells can be left behind during resection. This project uses time-resolved fluorescence and diffuse reflectance to differentiate cancerous and non-cancerous lung tissue. These differences could be used during surgical resection of tumor to ensure no positive margins are present. Using a bi-modal spectroscopy device, BEAR, optical properties were determined for 36 tumor, 36 fibrotic and 9 normal lung tissue samples. Most optical parameters showed statistically significant differences between tumor and other tissue types. Metabolic based optical parameters showed statistically significant differences between fibrotic and normal tissue while non-metabolic based parameters showed no difference. As surgical margins are likely to be between tumor and fibrotic tissue the results demonstrate success and promise for implementing this system. Future work using fresh samples would develop the system further and would be a step closer to in vivo use during surgery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".