A Systematic Review of Delirium Biomarkers and Their Alignment with the <scp>NIA‐AA</scp> Research Framework
Bibliographic record
Abstract
OBJECTIVES: To identify whether delirium biomarkers aligned with the National Institute on Aging-Alzheimer's Association (NIA-AA) research framework, a conceptual model that describes the use of diagnostic biomarkers for Alzheimer's disease and other related dementias (ADRD). DESIGN: Systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. SETTING: Acute care and outpatient settings. PARTICIPANTS: Adults diagnosed with delirium. METHODS AND MEASUREMENTS: MEDLINE, PsycInfo, Embase, and the Cochrane Library were searched for English-language studies published from January 2010 to February 2020. Studies included adults older than 18 years, identified delirium with a standardized assessment tool, and measured an ADRD biomarker. Independent reviewers determined whether an association between delirium and ADRD biomarker was found, the quality of biomarker data based on the REMARK (REporting recommendations for tumor MARKer prognostic studies) checklist, and the study bias based on the Newcastle-Ottawa Scale. RESULTS: A total of 61,256 citations were identified; 113 studies were included. Most studies did not examine amyloid, tau, or neurodegeneration biomarkers. Delirium may be associated with neurodegeneration biomarkers, but few to no studies found an association with amyloid and tau biomarkers. Delirium was not consistently associated with inflammatory biomarkers. The quality of biomarker data was moderate, and the risk of bias was moderate to high. Studies often did not collect prehospital and posthospital cognitive data. CONCLUSION: Most delirium diagnostic biomarker studies did not measure amyloid, tau, and/or neurodegenerative biomarkers, making characterization of the relationship between delirium and ADRD difficult. Future delirium biomarker diagnostic studies could improve the understanding of pathophysiologic links between delirium with other conditions affecting cognition.
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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 | 0.045 | 0.173 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".