Use of referral reply letters for continuing medical education: A review
Bibliographic record
Abstract
INTRODUCTION: Referrals between generalists and specialists are a central component of the health care system and necessitate effective communication between the involved providers. Despite the high prevalence of patient referrals and their crucial role in continuity and quality of care, the medical literature demonstrates that generalists may receive little or no information about the care their patients received and little information about the appropriateness of the referral or recommendations for follow-up care. General practitioners (GPs) prefer teaching that is directly related to their clinical work rather than traditional continuing education such as formal lectures. The purpose of this review is to assess the role of referral reply letters in the continuing education of GPs. METHODS: A comprehensive literature search was conducted to November 2001 using MEDLINE, EMBASE, the Cochrane Library, and the Research and Development Resource Base developed by Continuing Education, Faculty of Medicine, University of Toronto, to identify studies that examined the use of referral letters for the transfer of information from specialists to referring physicians. Data on methodology, unit of analysis, main outcome measures, and results were extracted. RESULTS: Of 1,250 articles retrieved, 9 met the eligibility criteria. Three of these analyzed the content of referral reply letters and 6 described the results of surveys of general and specialty physicians. DISCUSSION: Little educational content is currently included in letters from specialists to referring GPs. GPs are receptive to the use of referral replies as sources of learning.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".