SAT184 Improving Access To Osteoporosis Specialists Using Electronic Consultations
Bibliographic record
Abstract
Abstract Disclosure: C. Sethuram: None. W. Brown: None. G. Gill: None. C. Liddy: None. A. Afkham: None. E. Keely: None. Background: Timely access to osteoporosis specialists remains a challenge in Canada, where patients face long wait times for specialist care. Electronic consultations (eConsults) help address this issue by allowing primary care providers (PCPs) to pose clinical questions to specialists using a secure online platform. This study identifies the types of osteoporosis-related questions being asked by PCPs and describes the impact of the advice provided by osteoporosis specialists using eConsult. Methods: We performed a cross-sectional study of osteoporosis-related eConsults submitted to endocrinologists between January 2018 and December 2020 on the Champlain BASE™ eConsult Service in Ontario, Canada. Each eConsult was coded according to clinical question and answer type through consensus between two authors, based on pre-determined taxonomies established by the reviewers. We analyzed eConsult utilization data, including response times, PCP satisfaction, and referral outcomes, which were collected via PCP surveys following completion of the eConsult. Results: Of the 2534 eConsults sent to endocrinologists during the study period, 408 (16%) were specific to osteoporosis. The most common questions asked by PCPs were regarding whether or not to start treatment (18%), the initial therapy choice (13%), and how often to complete bone mineral density scans (8%). The most common responses from specialists included recommendations for bone mineral density scanning (13%), recommendation to start therapy (9%), and recommendation to treat using a bisphosphonate without the dose specified (9%). The median response interval was 3.1 days, and the median time spent by endocrinologists responding to the eConsult was 10.0 minutes. Eighty-four percent of cases were resolved without requiring an in-person referral. A course of action that PCPs already had in mind was confirmed in 42% of cases. Clear advice for a new course of action for PCPs to implement was provided in 54% of cases. Conclusion: Osteoporosis eConsults provide timely access to valuable specialist advice while avoiding unnecessary face-to-face clinic visits. Further, we identified commonly recurring osteoporosis questions asked by PCPs, which can be used to inform planning of future continuing professional development events. Presentation: Saturday, June 17, 2023
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.048 | 0.003 |
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".