Inspiratory Muscle Training for Individuals with Cervical Spinal Cord Injury or Chronic Obstructive Pulmonary Disease: A Survey of Canadian Physical Therapists
Bibliographic record
Abstract
Purpose: The objective of this study was to describe the current practice of Canadian physical therapists (PTs) using inspiratory muscle training (IMT) in the management of patients with cervical spinal cord injury (CSCI) or chronic obstructive pulmonary disease (COPD). Method: A postal survey was sent to all Canadian acute-care hospitals (. 250 beds) and to all centres providing rehabilitation for patients with CSCI or COPD. PTs were asked whether they used IMT and, if so, to describe patients for whom IMT was appropriate, as well as the devices and training protocols used. They were asked to list any contraindications to IMT. Results: One hundred nineteen questionnaires were sent to PTs treating patients with CSCI and 145 to PTs treating patients with COPD. The response rates were 70.6 per cent (CSCI) and 65.5 per cent (COPD). The rates of IMT use were 17.4 per cent (CSCI) and 4.7 per cent (COPD). The reasons for non-use included no knowledge about or training in IMT, a lack of resources, patients who were inappropriate for this treatment and no evidence of effectiveness. Conclusions: Few PTs are using IMT for patients with either CSCI or COPD. Little evidence exists of the effectiveness of IMT with CSCI patients, with stronger evidence of effectiveness in patients with COPD. A potentially effective modality for patients with COPD and CSCI, IMT appears to be underused by Canadian PTs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".