Gastroscopy for dyspepsia: Understanding primary care and gastroenterologist mental models of practice: A cognitive task analysis approach
Bibliographic record
Abstract
Background: Gastroscopy to investigate dyspepsia without alarm symptoms rarely results in clinically actionable findings or sustained health-related quality-of-life improvements among patients aged 18-60 years and is, therefore, not recommended. Despite this, referrals for and performance of gastroscopy among this patient population remain high. The purpose of this study was to understand family physicians' and gastroenterologists' mental models of dyspepsia and the drivers behind referring or performing gastroscopy. Methods: = 4). Results: Family physicians and gastroenterologists hold rich mental models of dyspepsia that rely on sensemaking; however, gaps in information continuity affect their ability to plan and coordinate patient care. Drivers behind decisions to refer or perform gastroscopy were: eliminating risk for serious pathology, providing reassurance, perceived preference by patients to receive information and reassurance from gastroenterologists, maintaining relationships with patients, and saving costs to the health system. Conclusions: Family physicians refer for dyspepsia when they are seeking support from gastroenterologists, they believe that alternative factors may be impacting the patient's health or view it as a cost-saving measure. Likewise, gastroenterologists perform gastroscopy for dyspepsia when they perceive it as a cost-saving measure, they want to support their primary care colleagues and provide their colleagues and patients with reassurance. An improved degree of communication between speciality and primary care could allow for continuity in the transfer of information about patients and reduce referrals for dyspepsia.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".