Improving Access in Gastroenterology: The Single Point of Entry Model for Referrals
Bibliographic record
Abstract
In 2005, a group of academic gastroenterologists in Calgary (Alberta) adopted a centralized referral intake system known as central triage. This system provided a single point of entry model (SEM) for referrals rather than the traditional system of individual practitioners managing their own referrals and queues. The goal of central triage was to improve wait times and referral management. In 2008, a similar system was developed in Edmonton at the University of Alberta Hospital (Edmonton, Alberta). SEMs have subsequently been adopted by numerous subspecialties throughout Alberta. There are many benefits of SEMs including improved access and reduced wait times. Understanding and measuring complex patient flow systems is key to improving access, and centralized intake systems provide an opportunity to better understand total demand and system bottlenecks. This knowledge is particularly important for specialties such as gastroenterology (GI), in which demand exceeds supply. While it is anticipated that SEMs will reduce wait times for GI care in Canada, the lack of sufficient resources to meet the demand for GI care necessitates additional strategies.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".