Advanced practice physiotherapy-led triage in Irish orthopaedic and rheumatology services: national data audit
Bibliographic record
Abstract
BACKGROUND: Many people with musculoskeletal (MSK) disorders wait several months or years for Consultant Doctor appointments, despite often not requiring medical or surgical interventions. To allow earlier patient access to orthopaedic and rheumatology services in Ireland, Advanced Practice Physiotherapists (APPs) were introduced at 16 major acute hospitals. This study performed the first national evaluation of APP triage services. METHOD: Throughout 2014, APPs (n = 22) entered clinical data on a national database. Analysis of these data using descriptive statistics determined patient wait times, Consultant Doctor involvement in clinical decisions, and patient clinical outcomes. Chi square tests were used to compare patient clinical outcomes across orthopaedic and rheumatology clinics. A pilot study at one site identified re-referral rates to orthopaedic/rheumatology services of patients managed by the APPs. RESULTS: In one year, 13,981 new patients accessed specialist orthopaedic and rheumatology consultations via the APP. Median wait time for an appointment was 5.6 months. Patients most commonly presented with knee (23%), lower back (22%) and shoulder (15%) disorders. APPs made autonomous clinical decisions regarding patient management at 77% of appointments, and managed patient care pathways without onward referral to Consultant Doctors in more than 80% of cases. Other onward clinical pathways recommended by APPs were: physiotherapy referrals (42%); clinical investigations (29%); injections administered (4%); and surgical listing (2%). Of those managed by the APP, the pilot study identified that only 6.5% of patients were re-referred within one year. CONCLUSION: This national evaluation of APP services demonstrated that the majority of patients assessed by an APP did not require onward referral for a Consultant Doctor appointment. Therefore, patients gained earlier access to orthopaedic and rheumatology consultations in secondary care, with most patients conservatively managed.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".