Assessment and Management of Patients with Chronic Cough by Certified Respiratory Educators: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The value of other health care professionals is increasingly being recognized to compensate for the shortage of physicians in Canada. Chronic cough is one of the most common reasons for consultation with a respirologist. In the present study, a prospective, randomized, controlled study was undertaken to determine whether Certified Respiratory Educators (CREs) could manage screened patients with chronic cough as effectively as respirologists. METHODS: An eight-week, prospective, parallel-design, randomized, controlled trial of the management of chronic cough patients was conducted. Patients were screened to exclude those with potentially life-threatening conditions. The primary outcome was the number of patients whose cough resolved or subjectively improved. RESULTS: A total of 198 patients were randomly assigned, and eight-week data were available on 151 patients. Mean age of the patients was 49.8+/-13.4 years, 70.2% were female and median cough duration was 16 months. The screening process was effective and referral wait times decreased from a median of two months to less than four weeks (P<0.0001). The educators averaged 4.9 contacts per patient compared with 2.7 by the physicians over the eight-week study period (P<0.0001). Most patients had had multiple therapeutic trials before referral. Cough resolved or improved in two-thirds of the patients at eight weeks; however, more patients showed improvement in the educator arm than in the physician arm, P<0.02. Cough-specific quality of life improved similarly in the two study arms at eight weeks (physician arm: 61.5+/-14.1 to 52.6+/-14.4, P<0.0001; CRE arm: 58.1+/-14.9 to 50.0+/-15.8, P=0.0003). CONCLUSIONS: CREs can safely and effectively assess, as well as appropriately treat, screened patients with chronic cough with a resultant reduction in wait times.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".