The SENSOR Study: Protocol for a Mixed-Methods Study of Self-Management Checks to Predict Exacerbations of Pseudomonas Aeruginosa in Patients with Long-Term Respiratory Conditions
Bibliographic record
Abstract
BACKGROUND: There are an estimated three million people in the United Kingdom with chronic obstructive pulmonary disease (COPD), and the incidence of bronchiectasis is estimated at around 0.1% but is more common in COPD and severe asthma. Both COPD and bronchiectasis are characterized by exacerbations in which bacteria play a central role. Pseudomonas aeruginosa is isolated from sputum samples from 4% to 15% of adults with COPD and is more likely to be isolated from patients with severe disease. Earlier detection of exacerbations may improve morbidity and mortality by expediting treatment. Aseptika Ltd has developed a system for patients to self-monitor important physiological measurements including levels of physical activity, peak flow, forced expiratory volume (FEV1), and biomarkers for P aeruginosa in sputum. OBJECTIVE: We aim to test this system in 20 participants with P aeruginosa colonization and 10 controls with Haemophilus influenzae. METHODS: We plan to recruit 30 adult participants with COPD or non-CF bronchiectasis who have cultured P aeruginosa or H influenzae during an exacerbation in the last 6 months. They must produce sputum on most days and should have been stable for 4 weeks prior to entry. Daily data collected will include symptoms, health care usage, medication, weight, FEV1, physical activity level, blood pressure, oxygen saturation, and temperature. Sputum and urine samples will be provided daily. These data will be analyzed to assess predictive value in detecting upcoming exacerbations. Qualitative data will be gathered through self-administered questionnaires and semistructured interviews to gather information on participant coping and their use of the technology involved. RESULTS: Recruitment has been completed and results from the study should be available at the end of 2017. CONCLUSIONS: The SENSOR study aims to test a home-monitoring system in people with chronic airway infection and is currently underway.
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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 | 0.024 | 0.018 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.067 | 0.016 |
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".