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Record W4380085417 · doi:10.3389/fneur.2023.1179243

Meta-analysis of serological biomarkers at hospital admission for the likelihood of developing delirium during hospitalization

2023· article· en· W4380085417 on OpenAlexafffund
Thiago Bassi, Elizabeth Rohrs, Michelle Nicholas, Steven Reynolds

Bibliographic record

VenueFrontiers in Neurology · 2023
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsSimon Fraser University
FundersMitacs
KeywordsDeliriumMedicineCochrane LibraryMeta-analysisMEDLINESystematic reviewChecklistData extractionEmergency medicineIntensive care medicinePediatricsInternal medicinePsychology

Abstract

fetched live from OpenAlex

Importance Identifying biomarkers that, at hospital admission, predict subsequent delirium will help to focus our clinical efforts on prevention and management. Objective The study aimed to investigate biomarkers at hospital admission that may be associated with delirium during hospitalization. Data sources A librarian at the Fraser Health Authority Health Sciences Library performed searches from 28 June 2021 to 9 July 2021, using the following sources: Medline, EMBASE, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Cochrane Methodology Register, and the Database of Abstracts of Reviews and Effects. Study selection The inclusion criteria were articles in English that investigated the link between serum concentration of biomarkers at hospital admission and delirium during hospitalization. Exclusion criteria were single case reports, case series, comments, editorials, letters to the editor, articles that were not relevant to the review objective, and articles concerning pediatrics. After excluding duplicates, 55 studies were included. Data extraction and synthesis This meta-analysis followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Independent extraction, with the consensus of multiple reviewers, was used to determine the final studies included. The weight and heterogeneity of the manuscripts were calculated using inverse covariance with a random-effects model. Main outcome(s) and measure(s) Differences in mean serum concentration of biomarkers at hospital admission between patients who did and did not develop delirium during hospitalization. Results Our search found evidence that patients who developed delirium during hospitalization had, at hospital admission, significantly greater concentrations of certain inflammatory biomarkers and one blood–brain barrier leakage marker than patients who did not develop delirium during hospitalization (differences in the mean: cortisol: 3.36 ng/ml, p < 0.0001; CRP: 41.39 mg/L, p < 0.00001; IL-6: 24.05 pg/ml, p < 0.00001; S100β 0.07 ng/ml, p < 0.00001). These differences were independent of other confounding variables such as the patient's severity of illness. A significantly lower serum concentration, at hospital admission, of acetylcholinesterase (difference in the means −0.86 U/ml, p = 0.004) was also associated with an increased vulnerability to developing delirium during hospitalization. Conclusion and relevance Our meta-analysis supports the hypothesis that patients with hypothalamic-pituitary axis dysfunction, increased blood–brain barrier permeability, and chronic overload of the cholinergic system, at hospital admission, are more vulnerable to developing delirium during hospitalization.

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 imitation

Not 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.

metaresearch head score (Codex)0.032
metaresearch head score (Gemma)0.089
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.089
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0200.076
Bibliometrics0.0090.008
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0030.002
Research integrity0.0040.003
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 head0.035
GPT teacher head0.286
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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

Citations8
Published2023
Admission routes2
Has abstractyes

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