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Record W2907115363 · doi:10.1097/pec.0000000000001712

Point-of-Care Ultrasound in Pediatric Diagnostic Dilemmas

2019· review· en· W2907115363 on OpenAlexaff
Priya Sharma, Faisal Al‐Sani, Sidharth Saini, Tais Sao Pedro, Peter Wong, Yousef Etoom

Bibliographic record

VenuePediatric Emergency Care · 2019
Typereview
Languageen
FieldMedicine
TopicGastrointestinal disorders and treatments
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineIntussusception (medical disorder)Point of care ultrasoundPerforationEmergency departmentPresentation (obstetrics)Emergency physicianBowel perforationPediatric emergency medicinePediatric SurgeonBowel obstructionIntensive care medicineMedical emergencyUltrasoundGeneral surgeryPediatricsRadiologyPediatric surgerySurgeryComplicationNursing

Abstract

fetched live from OpenAlex

Diagnostic dilemmas are ubiquitous in pediatric emergency medicine because of the varied and often insidious presentations of many pediatric conditions. Point-of-care ultrasound (POCUS) in emergency departments is being used for some of these diagnostic challenges and can often provide rapid and valuable information to supplement a physician's clinical assessment. Intussusception is a pediatric condition that may be challenging to recognize because of its subtle and varied presenting symptoms. An unrecognized or delayed diagnosis of intussusception can be catastrophic, with complications including bowel obstruction, bowel perforation, and death. Here we present two challenging cases of intussusception, one due to its atypical age of presentation and the other due to its unique symptoms. These cases demonstrate the benefits of point-of-care ultrasound for diagnostically challenging cases in the emergency department.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.030
GPT teacher head0.328
Teacher spread0.298 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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