Oxidative stress in operating room personnel: occupational exposure to anesthetic gases
Bibliographic record
Abstract
Health professionals exposed to anesthetic gases are at higher risk of reproductive, neurological, hematological, immunological, hepatic and renal system diseases. We investigated if oxidative stress induced by chronic exposure to anesthetic gases has any association with this matter. Plasma lipid peroxidation, total antioxidant capacity and total thiol molecule levels were measured in 66 operating room staff in comparison with 66 controls. The exposed group had a significantly higher level of lipid peroxidation with decreased thiol groups compared to control subjects. Total antioxidant capacity of the body was no different among exposed and not exposed subjects. Increased lipid peroxidation in the blood of exposed subjects warns that oxygen free radicals have increased in the body and thus might attack cells, which, in the long-term, results in multi-organ damage. The remaining blood total antioxidant capacity at normal values is promising and means that other non-thiol antioxidants, such as uric acid, transferrin, ceruloplasmin, albumin, and vitamin antioxidants, such as alpha-tocopherol and ascorbic acid, have been stimulated to maintain the total anti-oxidant power of the body at normal state.
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 | 0.000 | 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".