Need for improvement in the evaluation of pre‐malignant upper gastro‐intestinal lesions in India: Results of a nationwide survey
Bibliographic record
Abstract
BACKGROUND AND AIM: Gastric and esophageal cancers are associated with high morbidity in India. In the absence of formal screening programs in India, it is essential that all elective esophago-gastro-duodenoscopies (EGDs), irrespective of indication, be also considered an opportunity to screen for premalignant lesions. With this premise, we tried to assess the adherence to best practices in the detection of premalignant upper gastro-intestinal lesions (PMUGIL) among endoscopists in India. We also evaluated the adequacy of training, availability of appropriate facilities, and differences between teaching and non-teaching centers. METHODS: We disbursed a survey among endoscopists working in India, through the membership database of the Society of Gastrointestinal Endoscopists of India, by email and instant messaging. The responses were collected and subsequently analyzed. RESULTS: We obtained a total of 422 eligible responses. The adherence to best practices assessed was lower than the set threshold in all except one parameter in both teaching centers and non-teaching centers. Only 58.5% of endoscopists had received training in the detection of PMUGIL. Appropriate image enhanced endoscopy (IEE) facilities were available to only 58.05% of surveyed endoscopists. CONCLUSIONS: Strategies to improve detection of PMUGIL should be directed at improving adherence to best practices, ensuring adequate training of endoscopists in the evaluation of PMUGIL and improving infrastructure.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".