Orthopaedic Outreach: An innovative programme for orthopaedic patients in remote areas of Newfoundland and Labrador
Bibliographic record
Abstract
Introduction: Wait times to see an orthopaedic consultant can be lengthy. Remote communities such as Labrador City and Goose Bay, located in Labrador in the province of Newfoundland and Labrador, often do not have an orthopaedic specialist locally and patients are required to travel great distances to attend clinic appointments. The objectives of this report are to describe our Orthopaedic Outreach Programme where patients receive fracture assessments and care for musculoskeletal concerns at two local clinics by a visiting orthopaedic surgeon. We also describe the justification for the Orthopaedic Outreach Programme and list the benefits; financial and otherwise. Methods: December 2019 including demographics and procedures completed. Travel and hotel costs were estimated. Results: Over the last 5 years, the Orthopaedic Outreach Programme treated 1,698 patients at the 2 clinics. Cost savings were estimated at $366,768 per annum. The cost savings over the last 5 years were estimated at a total of $1,833,840. This does not account for patient's time off work and lost revenue that would occur when they make the trip to St John's for a clinic appointment. Conclusions: Our Orthopaedic Outreach Programme was implemented to improve access to orthopaedic services in the remote areas of Labrador. This report aims to describe the result of a programme focused on providing orthopaedic care to individuals who would otherwise be required to travel great distances for their care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".