Examination of Plastic Surgery Clinical Questions and Responses via an Electronic Consultation (eConsult) Service
Bibliographic record
Abstract
Introduction: Average wait times for plastic surgery depend on priority, but access to specialist consultation can be upwards of 1-2 years for elective referrals. The Champlain eConsult BASE™ system was developed in 2010 and is a PHIPA-compliant system that allows primary care providers to electronically send specialists questions about specific patients, potentially avoiding the need for a formal in-person consultation. Methods: Electronic Consults (eConsults) through the Champlain eConsult BASE™ system to plastic surgery from January 2021 to December 2022 were assessed by 2 reviewers. A standardized data extraction form was used, categorizing consults for question type and clinical problem. A mandatory close-out survey allowed for analysis on referring physician satisfaction, referral outcome, and impact on patient care. Results: Three hundred and thirty-one eConsults were included and were answered in an average of 2.1 ± 3.1 days. Specialists spent a mean of 14.0 ± 5.7 minutes per case. The most common content of the consults was related to hand trauma (37%), non-hand skin/soft tissue lesions (13%), and hand masses/lesions (bony or soft tissue) (8%). A formal consultation was avoided in 32%. Thirty-nine percent of cases resulted in a change in management: a referral to plastic surgery was avoided but originally contemplated by the family physician in 32%, and a referral was recommended but not originally contemplated in 7%. Conclusions: Our study demonstrates the potential of eConsults to facilitate timely consultation and avoid unnecessary formal consultations with plastic surgeons. This may reduce wait times and improve access to plastic surgeon services.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".