O24‐5: Altered megakaryocyte and platelet parameters in idiopathic pulmonary fibrosis
Bibliographic record
Abstract
Rationale: The role of megakaryocytes and platelets in idiopathic pulmonary fibrosis (IPF) is ill-defined. We sought to investigate whether megakaryocyte/platelet gene signature and/or platelet parameters (i.e., platelet counts, mean platelet volume [MPV]) in peripheral blood predicts outcome in IPF. Methods: Blood transcriptome data of IPF patients in the NCBI Gene Expression Omnibus (GEO) repository GSE93606 were analyzed. Enrichment of megakaryocytes and platelets in the blood were estimated using xCell, a novel computational method that assesses enrichment of individual cell types based on gene expression profile. We compared the disease progression-free survival between patients with high megakaryocyte/platelet enrichment score and patients with low enrichment score, using log-rank test. We also compared the overall survival between patients with high platelet parameters and patients with low platelet parameters in our IPF clinic, using log-rank test. A p-value <0.05 was considered statistically significant. Results: IPF patients with higher megakaryocyte score (above median) in blood transcriptome had lower disease progression-free survival than IPF patients with lower megakaryocyte score (below median) (p=0.0096). IPF patients with higher platelet counts had lower 2-year survivals than IPF patients with lower platelet counts in our clinic (p=0.0384). Conclusion: Blood transcriptome enriched with the megakaryocyte gene signature and higher platelet counts predict poor outcome in IPF. These data suggest that a megakaryocyte gene signature and platelet counts in peripheral blood may be novel biomarkers in IPF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".