Impact of Question Content on e-Consultation Outcomes
Bibliographic record
Abstract
BACKGROUND: By facilitating direct communication of primary care providers (PCPs) with specialists for advice, electronic consult (e-consult) services can reduce the need for patients to wait for and travel to face-to-face consultations with specialists. An association between avoiding face-to-face referrals using an e-consult service and specific content within each e-consult has not been rigorously explored. MATERIALS AND METHODS: Cases submitted to the Champlain Building Access to Specialists through eConsultation service between April 2011 to May 2013 were evaluated. Factors analyzed include question type (e.g., diagnosis or management), formulation (if interventions or outcomes were specified), and the addressed specialty. An avoided referral was present if the PCP indicated so in a mandatory close-out survey. A discrepancy was present if the PCP made a referral when the specialist did not indicate one was necessary, or if the PCP did not request a referral despite the specialist recommending one. RESULTS: There were 426 (40%) avoided referrals among 1,055 cases analyzed. Questions associated with the highest avoided referral rates included ones pertaining to diagnosis (44%), nonspecific requests for direction (44%), questions without specified interventions or outcomes (47%), and dermatology cases (49.5%). Specialists agreed on the need for a referral in 82% of cases, with most discrepancies due to the PCP making a referral without the specialist recommending one. CONCLUSIONS: Referral outcomes are associated with the type of question being asked, the formulation of each question, and the specialty being addressed. Discrepancies among PCPs and specialists regarding which patients require face-to-face referrals may help identify knowledge gaps and guide professional development.
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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 | 0.038 | 0.280 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".