Evaluation of proteins in natural rubber latex gloves and pulmonary function amongst female nurses in two tertiary hospitals in southern, Thailand.
Bibliographic record
Abstract
BACKGROUND: Inhaled natural rubber latex (NRL) allergens in a healthcare environment can cause NRL sensitization and reduce pulmonary functions. OBJECTIVES: To determine the amount of proteins and the effects of NRL gloves on the pulmonary functions of female nurses in two hospitals in the southern Thailand. METHODS: The study included 340 female nurses from two hospitals in which self-reported information and a blood sample were collected. NRL sensitization was determined by using a solid-phase enzyme-labeled fluoroenzyme-immunoassay for anti-NRLIgE antibodies. Proteins in NRL gloves were measured by a modified Lowry method. Pulmonary function was measured by a spirometer. RESULTS: The prevalences of respiratory effects self-attributed to NRL glove use and of NRL sensitization were 6.5 and 4.7%, respectively. Four of the 16 sensitized nurses reported respiratory symptoms. NRL sensitized nurses had lower forced expiratory flow (FEF25-75% predicted value) than those who were non-sensitized (Adj. difference = -12.56, 95% CI = -24.41 to -0.70). Furthermore, examination gloves contained protein levels in the range of 111-250 mg/dm2. Difference types of NRL glove contained with different amount of proteins.NRL sensitization was more prevalent in nurses with high concentrations of proteins in NRL gloves (p = 0.04). CONCLUSION: Sensitization to NRL was associated with a decrease in FEF25-75% predicted value, indicating narrowing of the small airways of the lung. Use of gloves with low proteins can reducing NRL allergen exposure in these hospitals, which may reduce the risk of developing respiratory problems and NRL sensitization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".