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Record W4238311154 · doi:10.31236/osf.io/9vjbc

A commentary on the importance of controlling for medication use within trials on the effects of exercise on depression and anxiety

2018· preprint· en· W4238311154 on OpenAlexaff
Paquito Bernard, Carayol Marion

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAnxiolyticAnxietyAntidepressantDepression (economics)MedicinePsychiatryDrugAnti-Anxiety AgentsPsychology

Abstract

fetched live from OpenAlex

Antidepressant and anxiolytics drugs may confound our understanding of the effects of exercise on anxiety and depression which may occur through biological pathways (some may act synergistically while others may be antagonistic), behavioural pathways (with indications of poorer exercise adherence for drug users), and indirect pathways (driven by deteriorated health status affecting the exercise capabilities of medication users). Therefore, the use of antidepressant or anxiolytic medications needs to be carefully considered in future studies assessing the effects of exercise on anxiety or depression.

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)0.064
metaresearch head score (Gemma)0.311
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.936
Threshold uncertainty score0.341

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0640.311
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0030.004
Science and technology studies0.0040.007
Scholarly communication0.0060.008
Open science0.0090.003
Research integrity0.0640.060
Insufficient payload (model declined to judge)0.0120.009

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 head0.050
GPT teacher head0.348
Teacher spread0.298 · 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.

Study designNot applicable
DomainMethods
GenreCommentary

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
Published2018
Admission routes1
Has abstractyes

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