Bronchoalveolar lavage fluid lymphocytosis in chronic hypersensitivity pneumonitis: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The role of bronchoalveolar lavage fluid (BALF) lymphocyte percentage in diagnosing chronic hypersensitivity pneumonitis (CHP) is unclear. We conducted a systematic review and meta-analysis of bronchoalveolar lavage (BAL) lymphocyte percentage in the diagnosis of CHP. METHODS: We searched Medline, Embase and the Cochrane Library from inception to August 2019. Individual patient data were obtained to test performance characteristics of BAL lymphocyte percentage at different thresholds. Random-effects models were used for pooled estimates, with comparisons made between CHP and non-CHP interstitial lung diseases (ILDs). RESULTS: =95.2%) in sarcoidosis. Results differed between CHP and IPF (p<0.0001), non-IPF IIP (p=0.0309) or CTD-ILD (p=0.0824), but not between CHP and sarcoidosis (p=0.0966). Using individual patient data from eight studies, a lymphocyte percentage threshold of >20% provided a sensitivity of 68.1% and a specificity of 64.8% for CHP. Higher thresholds provided lower sensitivity with higher specificity. Older age and ever having smoked were associated with lower lymphocyte percentage in CHP. CONCLUSIONS: BAL lymphocyte percentage is higher in CHP compared to IPF and other IIPs, with higher thresholds providing improved specificity at the cost of sensitivity. However, the parent studies are at risk of incorporation bias and prospective studies should evaluate the additive discriminate value of BAL lymphocyte percentage to accurately diagnose CHP.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".