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Record W2166942384

Psychosocial effect of common skin diseases.

2002· article· en· W2166942384 on OpenAlexaff
Benjamin Barankin, Joel G. DeKoven

Bibliographic record

VenuePubMed · 2002
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychosocialBiopsychosocial modelMedicineAcnePsoriasisAtopic dermatitisDiseaseAnxietyAffect (linguistics)ComorbidityPsychiatryDistressMEDLINEDermatologyDepression (economics)Clinical psychologyPsychologyPathology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To increase awareness of the psychosocial effect of acne, atopic dermatitis, and psoriasis. QUALITY OF EVIDENCE: A literature review was based on a MEDLINE search (1966 to 2000). Selected articles from the dermatologic and psychiatric literature, as well as other relevant medical journals, were reviewed and used as the basis for discussion of how skin disease affects patients' lives and of appropriate management. Studies in the medical literature provide mainly level III evidence predominantly based on descriptive studies and expert opinion. MAIN MESSAGE: Dermatologic problems can result in psychosocial effects that seriously affect patients' lives. More than a cosmetic nuisance, skin disease can produce anxiety, depression, and other psychological problems that affect patients' lives in ways comparable to arthritis or other disabling illnesses. An appreciation for the effects of sex, age, and location of lesions is important, as well as the bidirectional relationship between skin disease and psychological distress. This review focuses on the effects of three common skin diseases seen by family physicians: acne, atopic dermatitis, and psoriasis. CONCLUSION: How skin disease affects psychosocial well-being is underappreciated. Increased understanding of the psychiatric comorbidity associated with skin disease and a biopsychosocial approach to management will ultimately improve patients' lives.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.271
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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

Citations153
Published2002
Admission routes1
Has abstractyes

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