The association between question type and the outcomes of a Dermatology <scp>eC</scp>onsult service
Bibliographic record
Abstract
BACKGROUND: eConsult is a web based service that facilitates communication between primary care providers (PCPs) and specialists, which can reduce the need for face-to-face consultations with specialists. One example is the Champlain BASE (Building Access to Specialist through eConsultation) service with dermatology being the largest specialty consulted. METHODS: Dermatology eConsults submitted from July 2011 to January 2015 were reviewed. Post eConsult surveys for PCPs were analyzed to determine the number of traditional consults avoided and perceived value of eConsults. The time it took the PCP to receive a reply and the amount of time reported by the specialist to answer eConsult were proactively recorded and analyzed. A subset of 154 most recent eConsults was categorized for dermatology content and question type (e.g. diagnosis or management) using a validated taxonomy. RESULTS: A total of 965 eConsults were directed to dermatology from 217 unique PCPs. The majority of eConsults (64%) took the specialist between 10 and 15 minutes to answer. The overall value of this service to the provider was rated as very good or excellent in 95% of cases. In 49%, traditional in-person assessments were avoided. In the subset of the most recent cases, diagnosis was the most common question type asked (65.2%) followed by management (29%) and drug treatment (10.6%). The top five subject areas (40%) were: Dermatitis, Infections, Neoplasm, Nevi, and Pruritus. CONCLUSION: eConsults was feasible and well received by PCPs, which improves access to dermatology care with a potential to reduce wait times for traditional consultation.
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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 | 0.000 | 0.004 |
| 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 | 0.001 | 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 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".