Thyroid gland disturbances in pediatric shift work nurses employed in clinical hospital centre
Bibliographic record
Abstract
The aim of this paper is an attempt to explore the cause of thyroid gland disturbances in female pediatric nurses employed in a Pediatric Intensive Care Unit in an Eastern European hospital. A group of twenty hospital nurses was studied of whom fourteen suffered from some type of thyroid gland disturbances and a group of twenty primary care nurses with only sporadic incidence of thyroid disturbances. Regarding hypothyroidism they differ statistically significantly, p = .0399, as to obesity p = .0017, comparing thyroiditis p = .0374, and by goiter p = .008. Pediatric hospital nurses’ occupation requires contact with sick newborns and small children for 12-hour shifts. Thyroid gland disturbances are not fully explained, they vary from genetically origins, autoimmune processes, environmental stressors. Daily high level stress exposure of the mentioned hospital nurses can contribute to developing thyroid disturbances. Trained medical staff under the pressures of caring for this population may become sick. In such departments more nurses should be employed if shift work is performed under elevated stress. Signs of this stress might include inadequate nourishment causing obesity and cigarette smoking especially in night shifts. It is the task of Occupational Medicine to determine if stress exposure causes thyroid disturbances, especially in 12 hour shift workers, resulting in interventions to enhance preventive measures.
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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".