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Record W3086714300 · doi:10.5498/wjp.v10.i9.212

Delirium, insulin-like growth factor I, growth hormone in older inpatients

2020· article· en· W3086714300 on OpenAlexaboutno aff
Dimitrios Adamis, Iulian Coada, Piet Eikelenboom, Che‐Sheng Chu, Karen Finn, Vincent Melvin, J. Williams, David Meagher, Geraldine McCarthy

Bibliographic record

VenueWorld Journal of Psychiatry · 2020
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsDeliriumGeeDementiaObservational studyMedicineLongitudinal studyCognitive declineGeneralized estimating equationInternal medicineCognitionPediatricsPsychologyPsychiatryDiseasePathology

Abstract

fetched live from OpenAlex

BACKGROUND: negative feedback. AIM: To investigate the relationship between serum levels of IGF-I and GH, and the occurrence of delirium. METHODS: Prospective, longitudinal, observational study. Consecutive elderly inpatients (aged 70+), were assessed twice weekly with Montreal cognitive assessment (MoCA), Confusion assessment method (CAM), Acute Physiology and Chronic Health Evaluation II. Delirium was defined using CAM. Previous history of dementia was evaluated with the Informant Questionnaire on Cognitive Decline in the Elderly. IGF-I and GH levels were estimated with the ELISA method. Generalized estimating equations (GEE) model was applied for the first five assessments to analyze those longitudinal data. RESULTS: = 0.013 respectively). CONCLUSION: The results show that low levels of IGF-I, high levels of GH and low scores in cognition are independently associated with the occurrence of any delirium during the hospitalisation of medically ill older people. The results of the study supports the hypothesis that deficits in the immunoreactivity of the brain (low cerebral reserve) may be associated with delirium.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.256
Teacher spread0.243 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2020
Admission routes1
Has abstractyes

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