An advanced practice physiotherapy clinic in paediatric orthopaedics: A cost minimisation analysis
Bibliographic record
Abstract
PURPOSE: Advanced Practice Physiotherapy (APP) in paediatric orthopaedics is an effective adjunct to traditional consultant-led clinics, improving patient access to outpatient services, and reducing both waiting lists and times. However there has been no published economic evaluation of a paediatric orthopaedic APP service. This study performs a cost analysis, utilising a cost minimisation approach, comparing an APP Clinic in Paediatric Orthopaedics with usual care, from a health care perspective. METHODS: Data on all patients managed by the APP clinic for one calendar year were collected and outcomes and associated costs were calculated, including follow-up care. These costs were compared to the estimated costs of the usual care pathway, an Orthopaedic Consultant Elective Clinic (OCEC) and incremental savings per patient was calculated. RESULTS: A total of 534 patients attended the APP clinic for initial assessment during the calendar year 2017. The unit cost of a new appintment with the APP clinic is € 32.46 in comparison with € 56.98 for a new appointment in the OCEC. Our results demonstrate an incremental per patient saving of € 24.51 in favour of the APP clinic. Sensitivity analysis demonstrates that the cost savings obtained hold consistent in all cases, varying from € 23.13 to € 29.67 per patient in favour of the APP clinic pathway. This represents a cost saving of 43% for the APP Pathway over that of usual care. CONCLUSION(S): This is the first study to perform an economic analysis of the APP role in paediatric orthopaedics and demonstrates that an APP clinic for non-complex paediatric orthopaedic patients is substantially less costly than usual 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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".