Optical coherence tomography: Current biomedical applications and future clinical utility
Bibliographic record
Abstract
Clinicians are faced with an ever-increasing plethora of choices for the early detection of life-threatening medical conditions, such as heart disease or cancer. Many of these pathologies require invasive biopsy procedures to verify the presence and stage of disease progression. Once identified the patient must then undergo treatment, where current imaging techniques lack the resolution for treatment monitoring that can be correlated to the gold standard of disease-free survival, namely histology. Optical coherence tomography is an exciting, high-resolution (~10μm) non-invasive, imaging modality that may provide solutions to these problems and aid in the early detection and treatment monitoring of diseases. As the technology matures there is great potential for optical coherence tomography to become a clinical tool to aid in the clinical decision making process in an effort to properly provide patient risk stratification and subsequent appropriate therapy. In this article we present potential solutions to existing technical hurdles and speculate on future clinical implementation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".