New fiber-based approaches for optical biopsy (Conference Presentation)
Bibliographic record
Abstract
Optical biopsy of tissue using fiber optic probes has proven to be a powerful tool for non-invasive and minimally invasive diagnostics. However, there are still many challenges to improving diagnostic value and commercial translation of these techniques. Many fiber-based methods are limited by background noise, which impairs sensitivity and specificity. Aspects of quality control, such as adequacy of the target of interest sampled and validation of optical measurements with histopathology can be problematic. Complexity, cost, and disposability or sterilizability are roadblocks to widespread clinical use. Here, we present new approaches to using fibers for optical biopsy aimed at solving these problems. Specifically, the new concepts are designed with the goals of being simple and disposable, to improve control of light delivery and collection from the sample, and to inherently enable better quality control of the biopsy process. A concept-of-operation aimed at nearly zero impact to the work flow of the biopsy and standard pathology procedures will be outlined. Several concepts for fiber implementations will be presented. A trade-off analysis of the concepts used to select a first implementation for testing will be presented. Preliminary experimental validation in phantoms and tissue samples will be presented for the selected configuration.
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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 | 0.000 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".