Sodium nitrite poisoning: an emerging trend in Forensic Toxicology
Bibliographic record
Abstract
Background: Sodium nitrite (NaNO2) is an odorless, white crystalline powder soluble in water, and like common salt in appearance and taste. It can be toxic for humans and can cause methemoglobinemia [1]. Its mechanism of toxicity mainly consists in the oxidation of ferrous iron (Fe2+) to ferric iron (Fe3+) of one of the four heme structures in hemoglobin [2]. This is a growing topic due to the consecutive increase in the number of reported intoxication cases in recent years, mainly of suicide attempts by ingesting this powder. Objective: This study aims to summarize and characterize intentional and accidental sodium nitrite intake cases in what concerns to age, gender, and outcome (in particular mortality). Methods: A literature search was carried out on January 3, 2023, on PubMed. Only articles published in the last 5 years from the date of the search were selected. After excluding duplicate, off-topic, or no-access articles, 23 articles were selected, including 8 case reports, 8 case series and 6 review articles. Results: Of the 34 victims reported in the articles studied, 21 were male and 13 are female. The age range of the victims was from 16 to 70 years. 29 cases had an intentional character, while only 5 were caused by food poisoning. The amount of NaNO2 ingested was from 0.75 to 113 g. Conclusions: A patient presenting with cyanosis and unresponsive without respiratory disease should raise suspicions of sodium nitrite poisoning. There was a higher mortality rate for older victims, so age should be a conditioning factor for the victim survival/death [1]. In 48.3% of the cases, NaNO2 was obtained from the internet and online suicide forums. Thus, there should be limitation of this information on the internet and more control on NaNO2 sales. These measures are being implemented in some countries, such as Canada [3].
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".