Direct access physiotherapy to help manage patients with musculoskeletal disorders in an emergency department: results of a randomized controlled trial
Bibliographic record
Abstract
Abstract Context In several countries, physiotherapists (PT) have been integrated within emergency departments (EDs) to help manage patients with musculoskeletal disorders (MSKDs). Still, research on the effects of such initiatives is scarce. Objectives To evaluate the effects of direct access PT on MSKD patients consulting the ED in terms of clinical outcomes and use of health care resources. Design, Setting, Participants Randomized controlled trial, academic ED in Quebec City (Canada), participants 18-80 years presenting with a minor MSKD. Intervention Direct access PT at the ED Control Emergency Physicians lead management (EP). Main Outcome Measure Clinical outcomes (pain, interference of pain on function) and use of resources (ED return visit, interventions, diagnostic tests, consultations) were compared between groups at ED discharge and after 1 and 3 months using two-way ANOVAs, log-linear analysis and χ 2 tests. Results Seventy-eight patients suffering from MSKDs were included (40.2 ± 17.6 years old; 44% women). Participants in the PT group (n=40) had statistically lower levels of pain and pain interference at 1- and 3-months. They were recommended fewer imaging tests (38% vs. 78%; p<.0001) and prescription medication (43% vs. 67%; p=.030) at ED discharge, had used less prescription medication (32% vs. 72%; p=.002) and had revisited significantly less often the ED (0% vs. 21%; p=.007) at 1-month than those in the EP group (n=38). At 3 months, the PT group had used less over-the-counter medication (19% vs. 43%; p=.034). Conclusion Patients presenting with a MSKD to the ED with direct access to a PT had better clinical outcomes and used less services and resources than those in the usual care group after ED discharge and up to 3 months after discharge. The results of this study support the implementation of such models of care for the management of this population. Trial Registration This trial is registered at the US National Institutes of Health ( ClinicalTrials.gov ) # NCT04009369 Ethical approval This trial was approved by the Research Ethics Committee of the CHU de Québec - Université Laval #MP-20-2019-4307
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 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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".