A prickly situation: An attempted Caterpillar ingestion - case report
Bibliographic record
Abstract
BACKGROUND: Foreign body ingestion is a common problem in pediatrics. Each foreign body can present its' own unique challenges during removal, and we present the management of an ingested Spotted Tussock Moth (Lophocampa maculata), more commonly known as a caterpillar. CASE PRESENTATION: An 18-month-old boy presented to the emergency department with difficulty handling secretions and odynophagia. It was reported he had placed a caterpillar in his mouth and then spat it out. On examination, hundreds of miniscule filaments (setae) were seen embedded in his lips and tongue. Our service was consulted out of concern for airway involvement. The patient was taken to the operating room where a direct laryngoscopy under general anesthesia with spontaneous ventilation was performed to confirm the setae were confined to the anterior tongue and lips. Once we were satisfied the airway was stable, the airway was secured, and we then began to remove the setae. The initial method used was to use Adson-Brown forceps to remove the setae, however this proved difficult and time-consuming given the volume of setae and how thin the setae were. Ultimately, a more effective technique was developed: a 4 × 4 AMD-RITMES® gauze was applied to the mucosa in order to dry up any secretions and then a piece of pink, waterproof BSN medical® tape was applied to the mucosa. After 3 s of contact it was removed. This technique was then repeated and was used to remove the vast majority of the setae. CONCLUSION: To our knowledge, we have described the first technique to remove the caterpillar setae from the oral cavity mucosa in a fast, safe and efficient manner.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".