MétaCan
Menu
Back to cohort
Record W2902188457 · doi:10.3389/fpsyt.2018.00621

Increased Risk of Metabolic Syndrome in Antidepressants Users: A Mini Review

2018· review· en· W2902188457 on OpenAlexaboutno aff
Carla Gramaglia, Eleonora Gambaro, Giuseppe Bartolomei, P. Della Camera, Maira Chiarelli-Serra, Luca Lorenzini, Patrizia Zeppegno

Bibliographic record

VenueFrontiers in Psychiatry · 2018
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMetabolic syndromeAntipsychoticPsychiatryAntidepressantMedical prescriptionMEDLINESchizophrenia (object-oriented programming)Internal medicineObesityAnxietyPharmacology

Abstract

fetched live from OpenAlex

Mounting evidence has shown that the risk of metabolic syndrome (MetS) is substantially overlapping in the diagnostic subgroups of psychiatric disorders. While it is widely acknowledged that patients receiving antipsychotic medications are at higher risk of MetS than antipsychotic-naive ones, the association between antidepressants and MetS is still debated. The goal of our mini review was to analyse the relationship among depressive symptoms, antidepressant use and the occurrence of MetS. Adhering to PRISMA guidelines, we searched MEDLINE, reference lists and journals, using the following search string: ((("Mental Disorders"[Mesh]) AND "Metabolic Syndrome"[Mesh]) AND "Antidepressive Agents"[Mesh]), and retrieved 36 records. Two reviewers independently assessed records and the mini review eventually included the data extracted from 8 studies. The Newcastle-Ottawa Scale was used to assess the quality of the selected studies. Overall, the results of the mini review seem to support the association among depressive symptoms, antidepressants therapy and MetS. Except for H1-R high-affinity ones, the relationship between antidepressants and MetS still needs to be clarified. Considering the widespread prescription of antidepressants, both on behalf of psychiatrists and primary care physicians, further research on this topic is recommended.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.519
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.002
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 head0.026
GPT teacher head0.339
Teacher spread0.313 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations42
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in PsychiatrySame topicSchizophrenia research and treatmentFrench-language works237,207