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The importance of visualization of appendix on abdominal ultrasound for the diagnosis of appendicitis in children: A quality assessment review

2020· article· en· W3017225155 on OpenAlexaffabout
Muhammad A Hamid, Ruqiya Afroz, Uqba Nawaz Ahmed, Aneela Bawani, Dilnasheen Khan, Rabia Shahab, Asim Salim

Bibliographic record

VenueWorld Journal of Emergency Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicAppendicitis Diagnosis and Management
Canadian institutionsGrand River HospitalSt. Michael's HospitalThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsAppendixAppendicitisMedicineVisualizationUltrasoundAbdominal ultrasoundRadiologyAcute appendicitisAbdominal painGeneral surgerySurgeryArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Ultrasound has the first line investigation role in the diagnosis of acute appendicitis in children. The purpose of this study was to perform a quality assessment review on the visualization rate of appendix on ultrasound in children in the community hospital setting. METHODS: A retrospective chart review of the abdominal ultrasound findings for the visualization of the appendix was performed on paediatric patients ranging from 5 to 18 years. Data were collected from the two community hospitals of Toronto by using hospital electronic medical record for the ultrasound findings in patients presented with abdominal pain. RESULTS: <0.001). CONCLUSION: Visualization of an appendix on ultrasound increases the likelihood of correctly diagnosing appendicitis. In our study, we found low visualization rate of appendix on ultrasound that could be the result of many factors that contribute towards the low visualization rate of an appendix on ultrasound. Hence, the challenges in identifying appendix should be minimized to improve the visualization and diagnosis of appendicitis on ultrasound.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.080
GPT teacher head0.418
Teacher spread0.338 · 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.

Study designObservational
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

Citations13
Published2020
Admission routes2
Has abstractyes

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