Enhancing continuity of information: essential components of a referral document.
Bibliographic record
Abstract
OBJECTIVE: To identify elements of data that have been shown to contribute to continuity of information between primary care providers and medical specialists providing care to adult asthma patients. DESIGN: Systematic review of the literature followed by a 2-round modified Delphi consensus process. SETTING: Province of Ontario. PARTICIPANTS: Eight expert panelists, including 3 practising family physicians, a medical specialist knowledgeable in the treatment of asthma, a family physician previously involved in provincial initiatives related to primary care reform, an e-health technologist, a developer of evidence-based guidelines, and an operations and programs specialist. METHOD: We completed a systematic literature review to develop a list of items or data elements related to patient information transfer in chronic care. We engaged an 8-member expert panel in a 2-round modified Delphi process to assess the importance of the 74 data elements identified in the literature review and to identify any additional important elements. MAIN FINDINGS: The expert panelists reached consensus on 24 components of information, referred to here as minimum essential elements of a referral document, needed for consultations on adult asthma patients. CONCLUSION: The 24 minimum essential elements of information that should be transferred during referral of asthma patients from primary care providers to experts in asthma care were generated by primary care physicians and thought essential for achieving continuity in information transfer. We assembled these elements into a suggested format for a referral document. The format can be easily modified by practitioners caring for patients with other chronic diseases.
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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 | 0.174 | 0.291 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".