Chewing Gum in the Larynx: Foreign Body Aspiration or Iatrogenic Artifact? Challenges in Determining the Cause of Death in a Road Traffic Accident Victim With Resuscitation Intervention
Bibliographic record
Abstract
Identifying the cause of death in road traffic incidents and the contributing factors is crucial for forensic investigations, public health research, and epidemiological studies. In this case, the discovery of chewing gum in the larynx during an autopsy complicated the forensic diagnostic process and challenged the determination of the primary cause of death. Our case report details a 53-year-old male driver involved in a fatal road traffic accident. First responders found him unconscious and unresponsive. Despite resuscitative efforts, including endotracheal intubation, he could not be revived. The autopsy revealed multiple blunt force injuries from the collision and chewing gum in the larynx. The gum may have been aspirated while driving, potentially causing choking, severe coughing, or reflex cardiac arrest, which could have led to sudden incapacitation and the accident. Alternatively, the gum might have been overlooked during intubation, possibly pushing it deeper into the airway and creating an iatrogenic artifact. The cause of death was attributed to multiple blunt force injuries, specifically head trauma. However, the possibility of foreign body aspiration leading to the accident or the gum being an iatrogenic artifact cannot be ruled out. This case report highlights the potential impact of airway foreign bodies on road accidents and the risk of iatrogenic artifacts during resuscitation. It underscores the importance of thorough airway evaluation, prompt recognition of potential obstructions, and accurate documentation in prehospital settings to prevent worsening obstructions, misdiagnoses, delays in diagnosis, and complications in future cases.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".