Nurse-Led, Shared Medical Appointments for Common Gastrointestinal Conditions—Improving Outcomes Through Collaboration With Primary Care in the Medical Home: A Prospective Observational Study
Bibliographic record
Abstract
BACKGROUND: Gastroesophageal reflux disease (GERD), dyspepsia and irritable bowel syndrome (IBS) are common gastrointestinal disorders accounting for a significant demand for specialty care. The aim of this study was to evaluate safety, access and outcomes of patients assessed by a nurse-led, shared medical appointment. METHODS: This prospective observational study utilized a sample of 770 patients referred to a gastroenterology Central Access and Triage for routine GERD, dyspepsia or IBS from 2011 to 2014. Patient demographics, clinical indication, frequency and outcomes of endoscopy, quality of life, wait times and long-term outcomes (>2 years) were compared between 411 patients assigned to a nurse-led, shared medical appointment and 359 patients assigned to clinic for a gastroenterology physician consultation. RESULTS: The nurse-led, shared medical appointment pathway compared with usual care pathway had shorter median wait times (12.6 weeks versus 137.1 weeks, P < 0.0001), fewer endoscopic exams (50.9% versus 76.3%, P < 0.0001), less gastroenterology re-referrals (4.6% versus 15.6%, P < 0.0001), and reduced visits to the emergency department (6.1% versus 12.0%, P = 0.004). After two years of follow-up, outcomes were no different between the pathways. CONCLUSIONS: Patients with GERD, IBS or dyspepsia who attend the nurse-led, shared medical appointment have improved access to care and reduced resource utilization without increased risk of significant gastrointestinal outcomes after two years of follow-up.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".