Gastrovigilance: A Close Watch on Gastrointestinal and Hepatic Disorders- An Indian Perspective
Bibliographic record
Abstract
Gastrointestinal and hepatic disorders account for about 25% of consultations among general practitioners in India. Errors in clinical judgement and hesitancy in recommending necessary tests owing to lack of health insurance could result in delayed diagnosis and increased patient morbidity and mortality. Clinicians should thus be well equipped with effective strategies for skilful diagnosis and in a position to weigh the benefit-risk-ratio of recommending pertinent and disregarding less useful diagnostic tests. 'Gastrovigilance' includes disease-specific training for recognising risk factors, algorithms and referral pathways. This narrative review focuses on the common challenges or errors in managing these conditions in Indian clinical practice and their proposed solutions. Literature searches were performed using PubMed/MEDLINE and Google Scholar following the shortlisted gastrointestinal conditions. Based on the published literature and expertise of the senior gastroenterologists, improving disease-specific knowledge can enhance rates of correct diagnosis. Improved screening and patient education can reduce the risk of presentation at advanced stages and consequently improve prognosis. Another significant contributory factor is the patient-physician interaction which affects every stage of the disease management and methods to improve it, therefore vital in improving gastrointestinal and hepatic disease conditions. The most important means of improving gastrovigilance is optimising knowledge access in primary care. This shall improve diagnostic accuracy and reduce the burden of misdiagnosis. In the current narrative review, we have tried to elucidate the concept of gastrovigilance for gastrointestinal and hepatic conditions and substantiate it with published evidence.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".