Optical Biopsy Using Tissue Spectroscopy and Optical Coherence Tomography
Bibliographic record
Abstract
'Optical biopsy' or 'optical diagnostics' is a technique whereby light energy is used to obtain information about the structure and function of tissues without disrupting them. In fluorescence spectroscopy, light energy (usually provided by a laser) is used to excite tissues and the resulting fluorescence provides information about the target tissue. Its major gastrointestinal application has been in the evaluation of colonic polyps, in which it can reliably distinguish malignant from benign lesions. Optical coherence tomography (OCT) has been used in the investigation of Barrett's epithelium (and dysplasia), although a variety of other applications are feasible. For example, OCT could assist in the identification and staging of mucosal and submucosal neoplasms, the grading of inflammation in the stomach and intestine, the diagnosis of biliary tumours and the assessment of villous architecture. OCT differs from endoscopic ultrasound, a complementary modality, in that it has a much higher resolution but lesser depth of penetration. The images correlate with the histopathological appearance of tissues, and the addition of Doppler methods may enable it to evaluate the vascularity of tumours and the amount of blood flow in varices. Refinements in these new optical techniques will likely make them valuable in clinical practice, although their specific roles have yet to be determined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".