Clinical judgement of airway inflammation versus sputum cell counts in patients with asthma
Bibliographic record
Abstract
The inflammatory component of asthma is usually assessed indirectly by symptoms and spirometry, these may be inaccurate. It can now be assessed directly and reliably by the examination of sputum cell counts. There is no information on how clinical assessment of the presence and type of airway inflammation compares with actual measurements. In this single-centre observational study, sputum was collected from 76 consecutive adults with asthma attending a tertiary chest clinic after their physicians had recorded the expected cell counts in sputum. The authors examined the extent of agreement between clinical judgement of sputum cell counts and actual counts in asthmatic patients (Cohen's Kappa) and the possible predictors of agreement (multiple logistic regression). Sixty-seven of the 76 sputum samples were suitable for analysis. Agreement between expected and actual cell counts occurred in 30/67 patients. The overall agreement for the different cell types was poor (estimated K=0.14, 95% confidence interval (CI)=0.02, 0.26). The experience of the physician in using sputum cell counts in clinical practice, steroid requirement at the time of assessment, and control of asthma as assessed by the physician or by the patient could not predict the chances of agreement or disagreement. Unaware of the sputum results, the physicians often changed treatment in a way that seemed inappropriate for the cell counts present. There is poor agreement between clinical judgement of the presence and type of airway inflammation in asthmatic patients and sputum cell counts. The impact of sputum examination on the outcomes of anti-inflammatory treatment now needs investigation.
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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 | 0.028 | 0.172 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".