Upper Gastrointestinal Symptoms in Medical Professionals: A Higher Burden?
Bibliographic record
Abstract
OBJECTIVES: Gastroesophageal reflux disease (GERD) and dyspepsia cause significant healthcare resource utilization and greatly affect quality of life; therefore, a tendency exists to self-treat in lieu of formal diagnosis. Although their prevalence is established in the general population, their prevalence in physicians remains unknown. Our aim was to estimate the prevalence of GERD and dyspepsia symptoms in a sample of medical professionals by level of training. METHODS: An anonymous, computer-based questionnaire was distributed to medical students, residents/fellows, and attending physicians at our institution. The survey included symptom-based questions for GERD (Reflux Disease Questionnaire); quality-of-life questionnaire for dyspepsia (Nepean Dyspepsia Index [NDI]), and the Gastroparesis Cardinal Symptom Index (GCSI). We calculated scores based on the validated system from each questionnaire and used appropriate statistical analysis to evaluate associations between symptomatology and training level (medical students, residents/fellows, and attending physicians). RESULTS: Overall, there were 327 respondents (155 medical students, 127 residents/fellows, 45 attending physicians), of whom 52% were women, 56% were white non-Hispanic, 19% were Hispanic, and 74% were born in the United States/Canada. A total of 21% reported previously diagnosed GERD/dyspepsia, and 42% reported previously or currently using a medication for GERD/dyspepsia, with significantly higher use of proton pump inhibitors in attending physicians than in medical students and residents/fellows (P < 0.01). Higher median total scores for the NDI (P = 0.01) and GCSI (P < 0.01) in medical students exist compared with residents/fellows and attending physicians, with no significant differences for the Reflux Disease Questionnaire among groups. CONCLUSIONS: Although the prevalence of diagnosed GERD/dyspepsia in our sample was comparable to the general population, medication use was higher. Self-treatment with prescription medications increased with training. Median NDI and GCSI scores were inversely related to training level, which may reflect self-treatment in medical professionals with advanced training. Further study of GERD/dyspepsia in medical professionals is warranted.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".