Valued Components of a Consultant Letter from Referring Physicians’ Perspective: a Systematic Literature Synthesis
Bibliographic record
Abstract
BACKGROUND: Effective communication between the consultants and physicians form an integral foundation of effective and expert patient care. A broad review of the literature has not been undertaken to determine the components of a consultant's letter of most value to the referring physician. We aimed to identify the components of a consultant's letter preferred by referring physicians. METHODS: We searched Embase and MEDLINE (OVID) Medicine (EBM) Reviews and Cochrane Database of Systematic Reviews for English articles with no restriction on initial date to January 6, 2017. Articles containing letters from specialists to referring physicians regarding outpatient assessments with either an observational or experimental design were included. Studies were excluded if they pertained to communications from referring physicians to consultant specialists, or pertained to allied health professionals, inpatient documents, or opinion articles. We enumerated the frequencies with which three common themes were addressed, and the positive or negative nature of the comments. The three themes were the structure of consultant letters, their contents, and whether referring physicians and consultants shared a common opinion about the items. RESULTS: Eighteen articles were included in our synthesis. In 11 reports, 91% of respondents preferred structured formats. Other preferred structural features were problem lists and brevity (four reports each). The most preferred contents were oriented to insight: diagnosis, prognosis, and management plan (16/21 mentions in the top tertile). Data items such as history, physical examination, and medication lists were less important (1/23 mentions in the top tertile). Reports varied as to whether referring physicians and consultants shared common opinions about letter features. CONCLUSIONS: Referring physicians prefer brief, structured letters from consultants that feature diagnostic and prognostic opinions and management plans over unstructured letters that emphasize data elements such as detailed histories and medication lists. Whether these features improve outcomes is unknown.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".