Bibliographic record
Abstract
Introduction Despite the many technological advances that have occurred in the field of diagnostic endoscopy over the past few decades, remarkably the histological identification of dysplastic changes occurring within the gastrointestinal mucosa remains at present the best ”risk marker” for advancement to adenocarcinoma. On going research continues at the basic and clinical levels to evaluate putative molecular markers (i. e. fecal, serological, or urinary) which may facilitate the detection of early neoplastic disease, but unfortunately no such markers have been reported with reliable diagnostic value. Ideally, such tests would offer a means to better select asymptomatic patients for endoscopic examination, and the ability to identify subtle mucosal lesions allowing curative intervention either by ablation or minimally invasive surgery. Conventional endoscopic screening detects lesions in patients presenting with symptoms of obstruction, pain or bleeding due to cancer caused by lesions that are usually large and obvious with endoscopy or radiology, and at such an advanced stage are generally incurable. Unfortunately, conventional white light endoscopy is suboptimal at detecting dysplasia and is associated with a disproportionate miss rate for subtle lesions, e. g. flat adenomas. A further complication is the difficulty of detecting dysplasia within fields of transformed mucosa, such as Barrett’s esophagus and long standing chronic ulcerative colitis. These limitations present a significant clinical challenge and provide the incentive for development of new endoscopy systems to complement white light endoscopy. Novel photodiagnostic modalities are being developed and evaluated clinically for adjunctive use with standard endoscopy. These emerging technologies are based on the relative differences in the way light interacts with normal tissues and abnormal tissues which, during disease transformation, acquire altered optical properties. While conventional endoscopy is limited to detect lesions based on gross morphological changes, these new optically-based methods collectively offer a new strategy for endoscopic detection, ”bioendoscopy”, with the potential of detecting the very earliest mucosal changes at the microstructural, biochemical and molecular levels in realtime.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".