Mechanical insufflation-exsufflation: Practice patterns among respiratory therapists in Ontario.
Bibliographic record
Abstract
BACKGROUND: The mechanical insufflator-exsufflator (MIE) is effective in assisting cough and in helping to avoid unplanned hospitalizations, tracheostomy and long-term ventilation in patients with neuromuscular disease or spinal cord injury. Despite this, the availability and usage of the device in Canada is unknown. OBJECTIVE: To investigate practice patterns and availability of the MIE in Ontario hospitals. METHODS: A cross-sectional, self-administered mail survey was sent to a random sample of 400 respiratory therapists practicing in 96 Ontario hospitals. RESULTS: A total of 114 (28%) completed surveys were returned from 62 (65%) hospitals. Twenty (32%) hospitals had a MIE. The respiratory therapist was the predominant health care provider using the MIE. The device was most commonly used in the intensive care unit, and medical/surgical units in patients with neuromuscular diseases or spinal cord injuries. Optimal pressure spans of 35 cmH2O to 40 cmH2O were used by 54% of respondents. Fourteen of the 20 hospitals with an MIE had policies or guidelines in place, and four of these hospitals had established staff competencies. Measurements of peak cough flow, maximal inspiratory/expiratory pressure and vital capacity were reported to be infrequently performed. CONCLUSIONS: The present study demonstrated that the MIE device is not widely available in Ontario hospitals and there are variations in how the devices are applied, possibly resulting in suboptimal therapy. A comprehensive educational program about MIE devices that incorporates best practices and a practical component is recommended for current providers as well as for inclusion in student curricula.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".