P.057 Multidisciplinary Care for Optimal Management of Complex Nerve Injuries In Canada
Bibliographic record
Abstract
Background: Recent advances in management of peripheral nerve injuries is leading to a paradigm shift in the treatment of Canadian patients. Multi-disciplinary care models provide diagnostic, surgical and rehabilitative consultations within a single clinical encounter. Involvement of allied health care professionals has been shown to improve outcome. The purpose of this study was to ascertain the distribution and composition of multidisciplinary teams, and identify regional disparities. Methods: Representatives from clinics across Canada were invited to participate in a survey at the Annual Canadian Peripheral Nerve Symposium in London, Ontario in November 2019, with telephone follow up. Results: Delegates from 17 programs responded to the survey (12 academic centre and 5 community setting). Program provides electrodiagnostic testing, neuromuscular, rehabilitation and surgical assessment. Access to the following services was reported: occupational therapy=53% (9/17), physiotherapy 29% (5/17), research assistant=17% (3/17), social work=12% (2/17), mental health=6% (1/17). Conclusions: Complex nerve injury clinics are being established throughout Canada. Allied health care and research support are limited in many multi-disciplinary complex nerve injury programs. There is variable access, likely resulting in disparities in patient care across Canada. This data will be valuable for lobbying for resources for resources to improve the care of these complex patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".