Role of multiplex respiratory panel and procalcitonin in reducing antibiotic use for acute COPD exacerbations: a retrospective cohort study
Bibliographic record
Abstract
Multiplex respiratory pathogen testing (MR) and procalcitonin (PCT) may reduce antibiotic use in acute exacerbations of COPD (AECOPD). The impact of the combination of these tests on AECOPD management is uncertain. We aimed to assess the impact of both MR and PCT on antibiotic prescriptions in AECOPD. We hypothesize that both tests would independently reduce antibiotic use, but that their combination would not lead to a further reduction. This retrospective cohort study included adult patients who presented to the emergency department with AECOPD at a tertiary care center in Montreal, Canada. Three groups were analyzed: 1) pre-availability of PCT and MR (2014) 2) PCT availability only (2018) and 3) availability of both PCT and MR (2023). Data were collected from randomly selected patients’ medical record. Antibiotic use was compared across the three groups. 178 patients were included in total (65, 65 and 48 in groups 1,2 and 3, respectively). Antibiotics were prescribed to 69%, 45% and 69% of patients in group 1, 2 and 3, respectively. The difference was statistically significant between group 1 and 2 (p=0.005) , but it was not between group 1 and 3 (p=0.96). In group 2, antibiotics were prescribed to 67% of patients with positive PCT (≥0.1 mg/L) and to 38% of patients with negative PCT (p=0.048). In group 3, antibiotics were prescribed to 78% of patients with positive MR and to 63% of patients with negative MR (p=0.24). The use of PCT alone was associated with reduce antibiotic prescriptions in patients with AECOPD, but the addition of MR to PCT as an available diagnostic tool was not associated with further lower antibiotic usage.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".