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Record W1978883919 · doi:10.1097/der.0000000000000044

Latex Glove–Related Symptoms Among Health Care Workers: A Self-Report Questionnaire-Based Survey

2014· article· en· W1978883919 on OpenAlexvenueno aff
Waranya Boonchai, Wararat Sirikudta, Pacharee Iamtharachai, Pranee Kasemsarn

Bibliographic record

VenueDermatitis · 2014
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
FundersFaculty of Medicine Siriraj Hospital, Mahidol University
KeywordsMedicineAtopyLatex allergyAllergyAsthmaDermatologyInternal medicineImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: The use of latex gloves by health care workers (HCWs) can lead to multiple symptoms: eczema, contact urticaria, rhinitis, conjunctivitis, asthma, and anaphylaxis. OBJECTIVES: The objectives of this study were to reveal the prevalence of latex glove-related symptoms of HCWs at Siriraj Hospital and to determine risk factors associated with those symptoms associated with the use of latex gloves. METHODS: Self-administered questionnaires were sent to 6880 HCWs who were working at Siriraj Hospital and using latex rubber gloves in their duty. RESULTS: The questionnaire response rate was 65.8%. Of 4529 respondents, the male-to-female ratio was 1:8.6 and the mean age was 34.3 years. The majority of respondents were nurses (83%). The prevalence of glove-related symptoms among the HCWs is 13.3%. Glove-related cutaneous and noncutaneous symptoms were found in 11.3% and 5.9% of the respondents. CONCLUSIONS: The hospital housekeepers emerged as the job with the significantly higher prevalence rate of glove-related symptoms than that of the other job categories. Factors associated with glove-related cutaneous symptoms are frequency and duration of glove use, history of atopy, and history of allergy to fruit cross-reacting with latex. The quantity of glove use, history of atopy, and allergy to fruits cross-reacting with latex are risk factors for the occurrence of glove-related noncutaneous symptoms.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.008
GPT teacher head0.253
Teacher spread0.245 · 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 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

Citations18
Published2014
Admission routes1
Has abstractyes

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