A Multidisciplinary Hand Clinic Using Physiatrists for the Evaluation of Non-Operative Pathologies
Bibliographic record
Abstract
Purpose: The purpose of this study was to describe the impact of using a multidisciplinary hand clinic on (1) hand clinic waitlists for urgent operative pathologies and (2) the volume of urgent operative referrals seen by plastic surgery. Methods: A retrospective data analysis of all new referrals to the Peter Lougheed Centre hand clinic in Calgary, Alberta, was performed. Data were collected from 6 months before and after the introduction of the multidisciplinary model (ie, between January 2017 and January 2018). Demographics for all new referrals were collected from the clinic database, including wait times, triage type, and volume of referrals triaged to each discipline. Results: Prior to using a multidisciplinary model, 81% (n = 591) of new patient referrals were triaged directly to plastic surgery, 4% (n = 28) to physiotherapy, and 6% (n = 43) to minor surgery (N = 728). However, following the addition of physiatry to the clinic, 62% (n = 451) of new patient referrals were triaged directly to plastic surgery, 24% (n = 173) to physiatry, 2% (n = 17) to physiotherapy, and 4% (n = 31) to minor surgery (N = 730). Overall, the number of urgent operative referrals triaged to plastic surgery proportionally increased by 7%, from 67% to 74%. Mean wait times for urgent referrals to plastic surgery decreased by 1.7 ± 1.0 months ( P = .09). Conclusion: Applying a multidisciplinary model to a hand clinic can allow non-operative cases to be triaged directly to physiotherapy and physiatry, allowing plastic surgeons to manage a higher volume of urgent and operative referrals. Implementing a multidisciplinary hand clinic can, therefore, decrease waitlist volumes and shorten the time to assessment by a plastic surgeon. Type of Study: Level II Prognostic Study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".