Acute exacerbation of COPD in Emergency Room: just COPD exacerbations?
Bibliographic record
Abstract
Background: Severe exacerbations of COPD (AECOPD) often require Emergency Room (ER) visits and hospitalization. Because multimorbidities in smokers are frequent, their aggravation may contribute to the acute respiratory symptoms making the causative diagnosis of AE in ER difficult. Aim: To analyse the ER diagnosis in smokers presenting with respiratory AE and assess the factors influencing its accuracy when comparing with post-hospitalization diagnosis. Methods: 119 smokers (age 74±10) presenting to ER for acute worsening of respiratory symptoms were diagnosed of: AECOPD, AECOPD and heart failure (AECOPD+HF) and Other acute events and admitted to a Respiratory Ward (RW). Discharge diagnosis, obtained after complete evaluation, was then compared with ER diagnosis. Usefulness of WBC, PCR, BNP and ABG to diagnosis was assessed. Results: At RW discharge: AECOPD was diagnosed in 40% of cases, of which 69% had correct ER diagnosis; AECOPD+HF was diagnosed in 40%, of which 25% ER correct; and Other acute events in 20%, of which 57% ER correct. The RW final diagnosis by GOLD stage and the % of corresponding correct ER diagnosis are shown in figure. Importantly 58% of GOLD 1 had AECOPD+HF. Low BNP and PCR would favor AECOPD diagnosis (p<0.01). Conclusions: In a real-life scenario, ER diagnosis of the cause of acute exacerbations in smokers is difficult. A component of heart failure could be present in about 50% of cases, especially in GOLD 1.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".