Persistent Sputum Cellularity and Neutrophils May Predict Bronchiectasis
Bibliographic record
Abstract
BACKGROUND: Quantitative cell counts in sputum provide an accurate assessment of the type and severity of bronchitis. OBJECTIVE: To examine whether sputum cell counts could identify bronchiectasis in patients with recurrent bronchitis. METHODS: A retrospective survey of a clinical database (January 2004 to January 2005) of quantitative cell counts from sputum selected from expectorate in patients with obstructive airways diseases was used to identify predictors of bronchiectasis using ROC curves. This was prospectively evaluated (February 2005 to April 2008) using high-resolution computed tomography scans of thorax that were independently scored by a radiologist who was blinded to the clinical details. RESULTS: The retrospective survey identified 41 patients with bronchiectasis among 490 patients with airway diseases. Total cell count of 60 × 106⁄g or greater of the selected sputum with predominant neutrophils on two occasions had a sensitivity of 86.7%, a specificity of 87.5%, and positive and negative predictive values of 93% and 78%, respectively, to identify bronchiectasis. In the prospective study, 10 of 14 (71%) patients who met these criteria were identified to have bronchiectasis. Both total cell count and the percentage of neutrophils correlated with radiographic bronchiectasis severity. CONCLUSIONS: Persistent or recurrent intense sputum cellularity with neutrophilia is suggestive of bronchiectasis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".