Impact of Question Content on e-Consultation Outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| 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) | 0.000 | 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 it