Peripheral blood monocyte count and outcomes in patients with interstitial lung disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Peripheral blood monocyte counts have been associated with poor outcomes in interstitial lung disease (ILD). However, studies are limited by variable biomarker thresholds, analytic approaches and heterogenous populations. This systematic review and meta-analysis characterised the relationship between monocytes and clinical outcomes in ILD. Methods Electronic database searches were performed. Two reviewers screened abstracts and extracted data. Pooled estimates (hazard ratios (HRs)) of monocyte count thresholds were calculated for their association with mortality using ≥0.6×109and >0.9×109cells·L−1for unadjusted models and ≥0.95×109cells·L−1for adjusted models, using random effects, with heterogeneity and bias assessed. Disease progression associated with monocytes >0.9×109cells·L−1was also calculated. Results Of 3279 abstracts, 13 were included in the systematic review and eight in the meta-analysis. The pooled unadjusted HR for mortality for monocyte counts ≥0.6×109cells·L−1was 1.71 (95% CI 1.34–2.19, p<0.001, I2=0%) and for monocyte counts >0.90×109cells·L−1it was 2.44 (95% CI 1.53–3.87, p=0.0002, I2=52%). The pooled adjusted HR for mortality for monocyte counts ≥0.95×109cells·L−1was 1.93 (95% CI 1.24–3.01, p=0.0038 I2=69%). The pooled HR for disease progression associated with increased monocyte counts was 1.83 (95% CI 1.40–2.39, p<0.0001, I2=28%). Conclusions Peripheral blood monocyte counts were associated with an increased risk of mortality and disease progression in patients with ILD.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".