Efeitos ototóxicos da exposição ao monóxido de carbono: uma revisão
Bibliographic record
Abstract
BACKGROUND: Ototoxic effects of carbon monoxide exposure: a review. AIM: To analyze the literature on hearing and Carbon Monoxide (CO). METHODS: To describe the physical properties, the absorption, distribution and metabolism of CO, as well as its origin, production, sources and the international standards for occupational exposure. Several studies about the effects of CO exposure to the auditory system of humans and experimental animals were discussed. The main economic sectors where the combined exposure to noise and CO takes place were identified. A description of the basic CO toxic mechanisms that are able to raise occupational noise-induced hearing loss was given. CONCLUSION: The review of the literature indicated the following: 1. Examples of CO exposure sources include air pollution, smoking and second-hand smoking, and occupational exposures. 2. CO's main toxic mechanism can lead to hypoxia due to the conversion of oxyhemoglobin to carboxyhemoglobin. 3. Rats have been the most used experimental animals in CO auditory effects studies, this group of studies has demonstrated the combined effects of acute exposure to CO and noise. 4. Studies about the negative effects of CO exposure over the human auditory system were mainly carried out after acute exposures to CO. These studies did not control or report exposure to noise as a contributor to the observed hearing deficits. Currently the available evidence indicates the need for further research on the effects of CO exposure to the auditory system, alone or in combination with noise.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".