Emergency Laparoscopic-Assisted Gastrotomy for the Treatment of an Iron Bezoar
Bibliographic record
Abstract
Iron ingestion accounts for approximately 3% of calls to poison control centers. The profound local and systemic effects of an iron overdose have an associated mortality rate of 5%. Laparotomy and gastrotomy has been reported as a life-saving maneuver to extract the retained iron aggregates that are notoriously resistant to, removal by conventional emesis or lavage techniques. In this paper, we describe, for the first time, the use of laparoscopic-assisted gastrotomy in the treatment of an iron overdose. A 14-year-old girl attempted suicide by means of a polydrug drug overdose, which included ferrous fumarate, at a calculated potentially lethal dose of 70 mg/kg. A gastric iron bezoar was seen on plain radiograph. The regional poison control center recommended surgical removal of the retained iron tablets. Upper endoscopy confirmed the retention of iron and showed its dense adherence to the gastric mucosa. A 5-mm laparoscope was introduced at the umbilicus, and the stomach was grasped by an instrument introduced through a left-upper quadrant incision. The incision was then enlarged to allow the formation of a gastrotomy. The iron bezoar was removed with the aid of digital disimpaction and copious saline irrigation. The patient made a rapid postoperative recovery prior to undergoing psychiatric treatment. We conclude that laparoscopic-assisted gastrotomy is a simple and safe option in the acute management of a retained iron bezoar.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".