Fluorescence guided intraluminal endoscopy in the gastrointestinal tract: A systematic review
Bibliographic record
Abstract
BACKGROUND: Conventional endoscopy is based on full spectrum white light. However, different studies have investigated the use of fluorescence based endoscopy systems where the white light has been supplemented by infrared light and the use of relevant fluorophores. Fluorescence endoscopy utilizes the fluorescence emitted from a fluorophore, visualizing what is not visible to the naked eye. AIM: To explore the feasibility of fluorescence endoscopy and evaluate its use in diagnosing and evaluating gastrointestinal disease. METHODS: We followed the PRISMA guidelines for this systematic review. The research covered five databases; PubMed, Scopus, Web of Science, Embase, and the Cochrane Collection, including only studies in English and Scandinavian languages. Authors screened title and abstract for inclusion, subsequently full-text for inclusion according to eligibility criteria listed in the protocol. The risk of bias was assessed for all studies according to the Newcastle-Ottawa Scale. The authors extracted the data and reported the results in both text and tables. RESULTS: < 0.01). CONCLUSION: The use of fluorescence endoscopy is a promising method adding diagnostic value in the detection of neoplasia, adenomas, and assessment of tumor invasion within the gastrointestinal tract. More studies are needed to utilize the feasibility of fluorescence endoscopy compared with other endoscopic methods.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".