Evaluation of NinePoint Medical’s Nvision VLE device for gastrointestinal applications
Bibliographic record
Abstract
INTRODUCTION: The incidence of esophageal adenocarcinoma (EAC) has increased over the last few decades. With a known precursor lesion, Barrett's esophagus, this remains a target for screening and surveillance with the goal of detecting and providing curative treatment for early neoplasia. Areas covered: Current surveillance techniques rely on white light endoscopy and random tissue sampling which is time consuming, costly and prone to sampling error. Volumetric laser endomicroscopy (VLE), a second-generation optical coherence technology, has emerged as an advanced imaging modality with the potential to improve dysplasia detection, surveillance and subsequently prevent esophageal adenocarcinoma. This review will focus on the use of VLE for advanced imaging of Barrett's esophagus and summarize its current and potential uses elsewhere in the GI tract. Expert commentary: NinePoint's VLE imaging device enables imaging of large segments of BE facilitating identification of luminal and subsurface abnormalities that may have otherwise been missed. Its diagnostic accuracy is improving and laser-marking system adds the capacity for accurate VLE-histologic correlation. With the adoption of dysplasia scoring systems that utilize very few VLE imaging features, inexperienced endoscopists will likely be able to pick out areas concerning for dysplasia to target therapy.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".