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Record W2040683592 · doi:10.1089/lap.2008.0122.supp

Emergency Laparoscopic-Assisted Gastrotomy for the Treatment of an Iron Bezoar

2009· article· en· W2040683592 on OpenAlexaff
Fayza Haider, Claudio De Carli, Sonny Dhanani, Brian Sweeney

Bibliographic record

VenueJournal of Laparoendoscopic & Advanced Surgical Techniques · 2009
Typearticle
Languageen
FieldMedicine
TopicPoisoning and overdose treatments
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsBezoarMedicineSurgeryGastric lavageLaparotomyEndoscopyAnesthesia

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.354
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2009
Admission routes1
Has abstractyes

Explore more

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