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Record W2774883087 · doi:10.2147/ccid.s148499

The association between stress and acne among female medical students in Jeddah, Saudi Arabia

2017· article· en· W2774883087 on OpenAlexaff
Shadi Zari, Dana A. Alrahmani

Bibliographic record

VenueClinical Cosmetic and Investigational Dermatology · 2017
Typearticle
Languageen
FieldMedicine
TopicAcne and Rosacea Treatments and Effects
Canadian institutionsMcGill University
Fundersnot available
KeywordsAcneMedicineConfoundingInternal medicineCross-sectional studyClinical psychologyGrading (engineering)DermatologyPathologyBiology

Abstract

fetched live from OpenAlex

Introduction: Although there is widespread acceptance of a relationship between stress and acne, not many studies have been performed to assess this relationship. The objective of this study was to determine the relationship between stress and acne severity. Methods: A cross-sectional study was conducted among 144 6th year female medical students 22 to 24 years in age attending the medical faculty at King Abdulaziz University. This study used the global acne grading system (GAGS) to assess acne severity in relation to stress using the Perceived Stress Scale (PSS). The questionnaire also included some confounding factors involved in acne severity. Results: The results indicated an increase in stress severity strongly correlated with an increase in acne severity, which was statistically significant ( p <0.01). Subjects with higher stress scores, determined using the PSS, had higher acne severity when examined and graded using the GAGS. Conclusion: On the basis of this study, it is concluded that stress positively correlates with acne severity. Keywords: acne, acne vulgaris, acne severity, acne grade, stress, stress scale A Letter to the Editor has been received and published for this article.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.389
Teacher spread0.344 · 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.

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

Citations70
Published2017
Admission routes1
Has abstractyes

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