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Record W3136238379 · doi:10.4102/sajr.v25i1.2022

Clinical presentation, diagnosis and management of aerodigestive tract foreign bodies in the adult population: Part 1

2021· review· en· W3136238379 on OpenAlexaff
Rishi Philip Mathew, Sreekutty Sarasamma, Merin Jose, Ajith Toms, Vinayak Jayaram, Vimal Patel, Gavin Low

Bibliographic record

VenueSouth African Journal of Radiology · 2021
Typereview
Languageen
FieldMedicine
TopicForeign Body Medical Cases
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
Fundersnot available
KeywordsMedicineForeign bodyForeign BodiesImpactionForeign Body IngestionPopulationFish boneEsophagusPresentation (obstetrics)PathologySurgeryFish <Actinopterygii>Environmental health

Abstract

fetched live from OpenAlex

In the adult population, foreign bodies may be accidentally or intentionally ingested or even inserted into a body cavity. The majority of accidentally ingested foreign bodies pass through the alimentary tract without any complications and rarely require intervention. Accidentally ingested foreign bodies are usually fish bones, bones of other animals, and dentures. Oesophageal food impaction is the commonest cause of oesophageal foreign bodies in the Western hemisphere. Intentionally ingested foreign bodies may be organic or inorganic, and often require intervention; these patients have either underlying psychological or mental disease or are involved in illegal activities such as body packing, which involves trafficking narcotics. Imaging plays a crucial role in not only identifying the type, number and location of the foreign body but also in excluding any complications. In this comprehensive pictorial review, we provide an overview of the spectrum of foreign bodies ingested in adults, emphasising the role of various imaging modalities, their limitations and common foreign body mimickers on imaging.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.101
GPT teacher head0.396
Teacher spread0.295 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations20
Published2021
Admission routes1
Has abstractyes

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