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Record W2613043758 · doi:10.1097/yic.0000000000000179

Advanced glycation end products among patients maintained on antipsychotics

2017· article· en· W2613043758 on OpenAlexaff
Samer Hammoudeh, Suhaila Ghuloum, Ziyad Mahfoud, Arij Yehya, Dennis O. Mook‐Kanamori, Marjonneke J. Mook-Kanamori, Karsten Suhre, Abdulmoneim Abdulhakam, Azza Al-Mujalli, Yahya Hani, Reem El Sherbiny, Hassen Al‐Amin

Bibliographic record

VenueInternational Clinical Psychopharmacology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Glycation End Products research
Canadian institutionsInstitute of Health Services and Policy Research
FundersQatar National Research FundInternational Business Machines Corporation
KeywordsMedicineStatistical significanceSchizophrenia (object-oriented programming)Internal medicineProspective cohort studySignificant differenceGlycationPsychiatryPediatrics

Abstract

fetched live from OpenAlex

The aim of this study was to measure advanced glycation end products (AGEs) among participants maintained on antipsychotics using the AGE Reader and to compare them with controls from the general population. Participants maintained on antipsychotics for at least 6 months were recruited from the Psychiatry Department at Rumailah Hospital, Doha, Qatar. Healthy controls were recruited from the primary healthcare centers in Doha, Qatar. AGEs of a total of 86 participants (48 patients and 38 controls) were recorded. Among the group maintained on antipsychotics, women, smokers, and Arabs had significantly higher AGEs levels compared with men, nonsmokers, and non-Arabs, respectively (P<0.05). The levels of AGEs were higher among the group of patients maintained on antipsychotics in comparison to controls; however, the difference did not reach statistical significance. This is the first study to examine AGEs in patients maintained on antipsychotics. Our findings showed that such patients do not differ significantly from controls comparing AGEs levels. Future investigations might need to consider recruiting a larger sample size using a prospective design.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.223
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.451
Teacher spread0.412 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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