Feasibility of mean platelet volume as a biomarker for chronic obstructive pulmonary disease: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective To evaluate the feasibility of mean platelet volume (MPV) as a biomarker for chronic obstructive pulmonary disease (COPD). Methods A systematic search for studies published up to March 2019 was performed in the PubMed and Web of Science databases. Three independent investigators screened the titles and abstracts of including studies according to eligibility criteria. The Newcastle–Ottawa Scale was used to assess the quality of eligible studies, and statistical analyses were performed using Review Manager version 5.3. Results A total of eight studies with 1230 COPD patients and 443 healthy controls were included in our meta-analysis. No significant differences in MPV level were identified in pairwise comparisons of the acute exacerbations of COPD (AECOPD), stable COPD, and control groups. Furthermore, no significant correlation was observed between MPV level and systemic inflammatory biomarkers. Conclusions MPV does not appear to represent a suitable biomarker of disease phase or inflammatory burden in COPD. However, future large-scale studies should be performed to further investigate the relationship between MPV and COPD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.031 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".