Advanced practice physiotherapy in paediatrics: Implementation results
Bibliographic record
Abstract
Objectives: This study aimed to evaluate the implementation of an advanced practice physiotherapist (APP) clinic in our paediatric institution and assess APP and orthopaedic surgeon satisfaction. Methods: In this retrospective cohort study, all patient records from the APP clinic's second year (March 2017 to March 2018) at CHU Sainte-Justine were reviewed. These were compared with the records of patients seen by orthopaedic surgeons within the gait clinic the year before implementing the clinic. The following data were collected: demographic, professional issuing referral, reason for referral, consultation delay, clinical impression, investigation, and treatment plan. We also documented every subsequent follow-up to rule out any diagnostic change and identify surgical patients. Clinician satisfaction was assessed by the Minnesota Satisfaction and PROBES Questionnaires along with a short electronic survey. Results: Four hundred and eighteen patients were assessed by APPs and 202 by orthopaedic surgeons. APPs managed patients independently in 92.6% of cases. Nearly 86% of patients were discharged following the initial visit, and 7.4% were referred to a physiotherapist. Only 1% of APP patients eventually required surgery compared with nearly 6% in the orthopaedic group. The mean waiting time for consultation was greater in the APP group (513.7 versus 264 days). However, there was a significant reduction in mean waiting time over the last 3 months surveyed (106.5 days). Conclusions: The feedback from all clinicians involved was positive, with a greater mean score on the Minnesota Satisfaction and PROBES Questionnaire for APPs. The APP gait clinic appears to be an effective triage clinic. Level of evidence: III.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".