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Dermatologist and family practitioner practice patterns for occupational contact dermatitis

2007· article· en· W2074783073 on OpenAlexaffabout
D. Linn Holness, Shehrina Tabassum, Susan M. Tarlo, Gary M. Liss, Frances Silverman, Michael Manno

Bibliographic record

VenueAustralasian Journal of Dermatology · 2007
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineContact dermatitisDermatologyFamily medicineAllergyImmunology

Abstract

fetched live from OpenAlex

Medical practitioners have a role in the recognition of occupational contact dermatitis. The longer the duration of symptoms before diagnosis, the poorer the outcome. Our objective was to understand practice patterns, barriers and needs for early diagnosis of occupational contact dermatitis. A survey to obtain information on practice patterns was developed based on the literature and interviews with dermatologists and family practitioners. The survey was sent to all dermatologists and a random sample of 600 family practitioners in Ontario. Fifty-seven per cent of dermatologists and 9% of family practitioners report seeing more than 20 patients per year with occupational contact dermatitis. The majority of practitioners report taking a workplace exposure history. Barriers to taking a workplace exposure history include time constraints and lack of knowledge. Reasons for referral to specialists include a lack of expertise, testing facilities and knowledge about workers' compensation, time constraints and inadequate reimbursement, whereas lack of access to specialists is a barrier for referral. Although most practitioners identify a need for further education, a low volume of patients and time constraints are key barriers to continuing education. Opportunities are identified to improve educational initiatives and health services delivery for occupational contact dermatitis, tailored to each practitioner group.

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)0.001
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 score0.192
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.326
Teacher spread0.303 · 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

Citations13
Published2007
Admission routes2
Has abstractyes

Explore more

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