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Record W4404420111 · doi:10.24908/pocus.v9i2.17611

A Rare Case of Hemoperitoneum Diagnosed with Point of Care Ultrasound (POCUS)

2024· article· en· W4404420111 on OpenAlexvenueno aff
Jian Bai, Todd Cutler

Bibliographic record

VenuePOCUS Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicIntraperitoneal and Appendiceal Malignancies
Canadian institutionsnot available
Fundersnot available
KeywordsHemoperitoneumMedicineFocused assessment with sonography for traumaAscitesRadiologyUltrasoundEchogenicityPoint of care ultrasoundSurgeryAbdominal trauma

Abstract

fetched live from OpenAlex

A 68-year-old man presented with one week of vague abdominal symptoms and was found to have new ascites and pulmonary embolism for which a heparin drip was initiated. We report a case diagnosing hemoperitoneum with point of care ultrasound (POCUS). Identifying hemoperitoneum can be challenging, but POCUS can be a useful tool for its diagnosis. There is limited literature on the sonographic characteristics of hemoperitoneum. Echogenicity of fluid is not a reliable distinguisher between exudative and transudative effusions. The diagnosis of hemoperitoneum in this case was suggested by the progression of POCUS findings over time rather than sonographic characteristics by themselves.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.258
Teacher spread0.251 · 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 designCase report
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

Citations0
Published2024
Admission routes1
Has abstractyes

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