Physiotherapy Secretion Removal Techniques in People With Spinal Cord Injury: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To address whether secretion removal techniques increase airway clearance in people with chronic spinal cord injury (SCI). DATA SOURCES AND STUDY SELECTION: MEDLINE/PubMed, CINAHL, EMBASE, and PsycINFO were searched from inception to May 2009 for population keywords (spinal cord injury, paraplegia, tetraplegia, quadriplegia) paired with secretion removal-related interventions and outcomes. Inclusion criteria for articles were a research study, irrespective of design, that examined secretion removal in people with chronic SCI published in English. REVIEW METHODS: Two reviewers determined whether articles met the inclusion criteria, abstracted information, and performed a quality assessment using PEDro or Downs and Black criteria. Studies were then given a level of evidence based on a modified Sackett scale. RESULTS: Of 2416 abstracts and titles retrieved, 24 met the inclusion criteria. Subjects were young (mean, 31 years) and 84% were male. Most evidence was level 4 or 5 and only 2 studies were randomized controlled trials. Three reports described outcomes for secretion removal techniques in addition to cough, whereas most articles examined the immediate effects of various components of cough. Studies examining insufflation combined with manual assisted cough provided the most consistent, high-level evidence. Compelling recent evidence supports the use of respiratory muscle training or electrical stimulation of the expiratory muscles to facilitate airway clearance in people with SCI. CONCLUSION: Evidence supporting the use of secretion removal techniques in SCI, while positive, is limited and mostly of low level. Treatments that increase respiratory muscle force show promise as effective airway clearance techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".