A Systematic Review of Diagnostic Biomarkers of COPD Exacerbation
Bibliographic record
Abstract
The aims of this systematic review were to determine which blood-based molecules have been evaluated as possible biomarkers to diagnose chronic obstructive pulmonary disease (COPD) exacerbations (AECOPD) and to ascertain the quality of these biomarker publications. Patients of interest were those that have been diagnosed with COPD. MEDLINE, EMBASE, and CINAHL databases were searched systematically through February 2015 for publications relating to AECOPD diagnostic biomarkers. We used a modified guideline for the REporting of tumor MARKer Studies (mREMARK) to assess study quality. Additional components of quality included the reporting of findings in a replication cohort and the use of receiver-operating characteristics area-under-the curve statistics in evaluating performance. 59 studies were included, in which the most studied biomarkers were C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α). CRP showed consistent elevations in AECOPD compared to control subjects, while IL-6 and TNF-α had variable statistical significance and results. mREMARK scores ranged from 6 to 18 (median score of 13). 12 articles reported ROC analyses and only one study employed a replication cohort to confirm biomarker performance. Studies of AECOPD diagnostic biomarkers remain inconsistent in their reporting, with few studies employing ROC analyses and even fewer demonstrating replication in independent cohorts.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".