Upper Gastrointestinal Symptoms in Medical Professionals: A Higher Burden?
Bibliographic record
Abstract
Introduction: Gastroesophageal reflux diseases (GERD) and dyspepsia cause significant healthcare resource utilization and greatly impact quality of life (QOL); therefore, a tendency exists to self-treat in lieu of formal diagnosis. While its prevalence is established in the general population, that of GERD and dyspepsia in physicians remains unknown. Our goal was to estimate the prevalence of GERD and dyspepsia symptomatology and disease characteristics in a sample of medical professionals by level of training. Methods: An anonymous, computer-based questionnaire was distributed to medical students (MS), residents/fellows (RES), and attendings (ATT) at our institution. The survey included symptom-based questions for GERD (RDQ: reflux disease questionnaire. Am J Gastroenterol. 2012;107:1354-60); QOL questionnaire for dyspepsia (NDI: nepean dyspepsia index. Aliment Pharmacol Ther. 2001;15:207-16), and GCSI (gastroparesis cardinal symptom index. Aliment Pharmacol Ther. 2003;18:141-50). We calculated scores based on the validated system from each questionnaire, and used appropriate statistical analysis to evaluate associations between symptomatology and training level (MS, RES, ATT). IRB approval was obtained. Results: Three hundred twenty-seven respondents (155 MS, 127 RES, 45 ATT), 52% women, 56% white non-Hispanic, 19% Hispanic, and 74% U.S./Canadian, participated. Twenty-one percent reported previously diagnosed GERD/dyspepsia, while 42% reported previously or currently using a medication for GERD/dyspepsia, with significantly higher use of PPIs in ATT than MS/RES (p<0.01). Higher median total scores for the NDI (p=0.01) and GCSI (p<0.01) in MS exist compared to RES/ATT, with no significant differences for the RDQ amongst groups (Tables 1-2).TableConclusion: While the prevalence of diagnosed GERD/dyspepsia in our sample was comparable to the general population, medication use was higher. Self-treatment with prescription medications significantly increased with level of training. Median NDI and GCSI scores were inversely related to training level, which may reflect the results of self-treatment in advanced medical professionals. Further study of GERD/dyspepsia in medical professionals is warranted to enable effective diagnosis, improve healthcare resource utilization and their QOL.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.010 | 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".