Family physician access to specialist advice by telephone: Reduction in unnecessary specialist consultations and emergency department visits.
Bibliographic record
Abstract
PROBLEM ADDRESSED: Timely access to specialist care is an important issue for patients with mild to moderate symptoms, and wait times for referrals are currently quite long. OBJECTIVE OF PROGRAM: To provide FPs with quick telephone access to other specialists for treatment advice for patients with nonserious conditions that they would otherwise refer to specialist care. PROGRAM DESCRIPTION: The RACE (Rapid Access to Consultative Expertise) program is a telephone hot-line providing FPs and nurse practitioners in the Vancouver, BC, area with timely access to specialist consultations. An evaluation of data from RACE found 60% of RACE calls prevented patients from visiting a specialist and 32% of calls prevented FP referrals to hospital emergency departments. CONCLUSION: Supported by RACE, FPs can more effectively remain the locus of patient care, calling on other specialist expertise when appropriate and providing better coordination of care for their patients. Evaluations to date suggest RACE helps reduce system costs by reducing unnecessary emergency department visits and face-to-face specialist consultations.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".