Bibliographic record
Abstract
The detection, diagnosis and treatment of early cancers offers the best hope for the prevention and cure of gastrointestinal cancers – one of the leading causes of death worldwide (1). The detection of pre- or early cancer using white light endoscopy can be challenging because their morphology can be inconspicuous (ie, nonpolypoid; slightly elevated, flat, or slightly depressed]) and their colour can be minimally altered. Indeed, nonpolypoid neoplasms have been shown to be common and important in the esophagus, Barrett’s mucosa and stomach (Figure 1). Our recent prevalence study (2), highlighted their importance in the colon. We showed that nonpolypoid colorectal neoplasms (NP-CRNs) are relatively common and are potentially more dangerous than polypoid neoplasms of similar size because they have a higher risk of containing in situ or submucosal invasive carcinoma. Some nonpolypoid gastrointestinal neoplasms are fairly easy to detect and diagnose, whereas others can be quite difficult to visualize using white light illumination. The current technique and technology of image-enhanced endoscopy (IEE) is available to augment the detection, diagnosis and treatment of these subtle lesions. Figure 1) Distribution of superficial lesions according to morphological classification showing the relevance and importance of the nonpolypoid types in our endoscopic practice. Data regarding lesions of the upper gastrointestinal tract were derived from the Paris ... Dr Tonya Kaltenbach is a gastroenterologist with the VA Healthcare System in Palo Alto, California, USA There are two methods of IEE: dye-based and equipment-based (3). The objective of these two methods is to increase the contrast of structures, thus making the mucosal topography, morphology and borders of lesions viewable in finer detail. Used alone or in tandem, they may complement the white light examination as well each other (Figure 2). Detailed examination of the mucosa provides a cross-sectional view of the underlying pathology and facilitates discrimination between normal, non-neoplastic and neoplastic tissue. Taken together with size and morphology, and observation during submucosal injection, important information regarding the likelihood of submucosal invasion and whether the patient can undergo a safe and curative endoscopic resection is obtained. Figure 2) The benefit of the detection and diagnosis of pre- and early cancer of the gastrointestinal tract is significantly curtailed if safe and efficacious endoscopic treatment is unavailable. Image-enhanced endoscopy is an integral component of endoscopic diagnosis, ... The present article describes the techniques and applications of IEE and provides readers of the Journal with a resource to begin or to potentially improve on their use of IEE. For critical analysis of the literature, readers are directed toward a comprehensive review of the subject (3,4). We will present a description of the various techniques of IEE, and their specific preparations and properties. We will also include an outline of the supporting data regarding the use of IEE for the detection, diagnosis and therapy of a variety of nonpolypoid gastrointestinal neoplasms.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.134 | 0.105 |
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".