Electronic Consultation Systems: Impact on Pediatric Orthopaedic Care
Bibliographic record
Abstract
BACKGROUND: The demand for pediatric orthopaedic surgery consultation has grown rapidly, leading to longer wait times for elective consultation in some regions. Some specialties are addressing this increased demand through electronic consultation services. We wanted to examine the impact of pediatric orthopaedic e-consultations in Canada's Eastern Ontario region. METHODS: We developed a cross-sectional study of all the cases directed to a pediatric orthopaedic surgery specialist using the Champlain Building Access to Specialists through eConsultation (BASE) eConsult service over a 2-year period and examined their impact on in-person referrals, time of e-consultation and primary care satisfaction as well as types of clinical questions that were asked. RESULTS: Electronic consultations avoided in-person appointments in 68% of the submitted cases. The median response by specialists received by the primary care providers (PCPs) was <20 hours. A total of 69% of consultations involve >1 type of clinical questions, most commonly about basic trauma/fracture care and management recommendations. Ninety-seven percent of the PCPs found the overall value for the care of the patients to be good or excellent. CONCLUSIONS: This cross-sectional study demonstrates the effective and timely use of eConsult in pediatric orthopaedic surgery. It also shows a significant reduction in the number of in-person consultations required and demonstrates a high satisfaction rate by PCPs using the service. CLINICAL RELEVANCE: In addition to the efficacy and time-sensitive care provided to the patients, the study shows that, professionally, 89% of PCPs found this service to be excellent or good. The broader implications of electronic consultation on overall quality of care, population health, and patient satisfaction requires further investigation.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".