Comprehensive Care Management in Conjunction with Sputum Cytometry-Guided Pharmacotherapy in a Post-Discharge Clinic for Patients with COPD
Bibliographic record
Abstract
Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are amongst the most common reasons for hospital admission, and recurrent episodes occur frequently. Comprehensive care management (CCM) strategies have modest effect in preventing re-admissions. The objectives of this study were to examine the utility of optimizing anti-inflammatory therapy guided by sputum cytometry in the post-hospitalization setting, and to assess the feasibility and effectiveness of a clinic combining CCM and sputum-guided therapy. This is an observational study examining patients who received open-label CCM and sputum cytometry-guided pharmacotherapy in a COPD post-discharge clinic. Referral was based on high risk for readmission after hospitalization for AECOPD. The primary outcome was the change in COPD-related healthcare utilization before and after Visit 1, and this was analyzed with a mixed-effects negative binomial model controlling for age, number of follow-up clinic visits, pack years, current smoking and FEV1. Of 138 patients referred to the clinic, 73% attended at least one visit. Mean FEV1 was 42.8 (19.3) % predicted. Of the patients attending clinic, 42.6% produced an adequate sputum sample, and 32.7% had an abnormal sputum. By individual, infectious bronchitis was the most common (25.7%), followed by eosinophilic bronchitis (13.9%). Comparing the 6-months prior to and after the first clinic visit, there was a lower incidence rate ratio after visit 1 for COPD-related healthcare utilization (0.26 (95%CI 0.22,0.33; p < 0.001)). A COPD post-discharge clinic combining sputum-guided treatment and CCM was feasible and associated with a nearly 75% reduction in the incidence of COPD-related healthcare utilization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".