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Record W2990931921 · doi:10.1177/0300060519887886

Feasibility of mean platelet volume as a biomarker for chronic obstructive pulmonary disease: A systematic review and meta-analysis

2019· review· en· W2990931921 on OpenAlexaboutno aff
Yiming Ma, Dandan Zong, Zijie Zhan, Herui Li, Zhongshang Dai, Yanan Cui, Lijuan Luo, Zihang Zeng, Chenjie He, Yan Chen

Bibliographic record

VenueJournal of International Medical Research · 2019
Typereview
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMedicineCOPDMean platelet volumeBiomarkerMeta-analysisInternal medicinePulmonary diseasePhysical therapyPlatelet

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.587
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0080.006
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.228
GPT teacher head0.499
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2019
Admission routes1
Has abstractyes

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