Bibliographic record
Abstract
PURPOSE OF REVIEW: This review focuses on seafood workers engaged in harvesting, processing and food preparation. These groups are increasingly at risk of developing occupational allergy and respiratory disease as a result of seafood handling and processing activities. This review provides an update of a previous review conducted a decade ago. RECENT FINDINGS: Exposure characterization studies have demonstrated that aerosolization of seafood (muscle, visceral organs, skin/mucin) during canning and fishmeal operations result in highly variable levels of airborne particulate (0.001-11.293 mg/m3) and allergens (0.001-75.748 ug/m3). Occupational asthma is more commonly associated with shellfish (4-36%) than with bony fish (2-8%). Other seafood-associated biological (Anisakis) and chemical agents (protease enzymes, toxins and preservatives) have also been implicated. Atopy, smoking and level of exposure to allergens are significant risk factors for sensitization and the development of occupational asthma. Molecular studies of the allergens suggest that aside from tropomyosin and parvalbumin, other as yet uncharacterized allergens are important. SUMMARY: Future research needs to focus on detailed characterization of allergens in order to standardize exposure assessment techniques, which are key to assessing the impact of interventions. The clinical relevance of agents such as serine proteases and endotoxins in causing asthma through nonallergic mechanisms needs further epidemiological investigation.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".