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Data_Sheet_1_The Relationship Between Serum Cytokine Levels and the Degree of Psychosis and Cognitive Impairment in Patients With Methamphetamine-Associated Psychosis in Chinese Patients.xls

2020· dataset· en· W6908800755 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2020
Typedataset
Languageen
FieldSocial Sciences
TopicGerman Social Sciences and History
Canadian institutionsnot available
Fundersnot available
KeywordsPsychosisPositive and Negative Syndrome ScaleCognitionCytokineCognitive impairmentRecallMethamphetamineSchizophrenia (object-oriented programming)

Abstract

fetched live from OpenAlex

<p>Background: Cytokine levels can be changed in methamphetamine (METH) use disorders (MUDs) and primary psychosis. The present study assessed serum levels of some kinds of interleukins (ILs) in METH-associated psychosis (MAP) and their relationships with psychotic symptoms and cognitive dysfunction.</p><p>Methods: Serum IL-2R, IL-6, IL-8, and IL-10 levels were examined by chemiluminescence assays in MAP patients (n = 119) and healthy controls (n = 108). The Positive and Negative Syndrome Scale (PANSS) and Montreal Cognitive Assessment (MOCA) were administered.</p><p>Results: Serum levels of IL-6 and IL-8 were significantly increased in MAP patients (all p < 0.05). There was a negative relationship between IL-2R levels and PANSS positive (P) subscale scores (r = −0.193, p = 0.035). IL-6, IL-8 and IL-10 levels were all negatively correlated with the naming, delayed recall and orientation subscores on the MOCA (r = −0.209, p = 0.022; r = −0.245, p = 0.007; r = −0.505, p < 0.001, respectively).</p><p>Conclusions: Our results indicate that immune disturbances are related to MAP and that IL-2R, IL-6, IL-8, and IL-10 are associated with the severity of psychotic symptoms and cognitive function impairment.</p>

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.407
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.082
GPT teacher head0.318
Teacher spread0.235 · 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.

Study designObservational
Domainnot available
GenreDataset

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

Citations0
Published2020
Admission routes1
Has abstractyes

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