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Record W3215317843 · doi:10.24908/pocus.v6i2.15186

Real-time Point-of-care Ultrasound for the Diagnosis and Treatment of Testicular Torsion

2021· article· en· W3215317843 on OpenAlexvenueno aff
Rahul V. Nene, Rachna Subramony, Michael Marcias, Colleen Campbell, Amir Aminlari

Bibliographic record

VenuePOCUS Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicTesticular diseases and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTesticular torsionUltrasoundScrotumSpermatic Cord TorsionEmergency departmentScrotal PainTesticular painTesticleEmergency ultrasoundBlood flowRadiologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Background: Testicular torsion is a surgical emergency that needs prompt diagnosis and treatment. Point-of-Care ultrasound (POCUS) can not only establish the diagnosis but also guide the Emergency Physician in evaluating the response to manual detorsion. Case Report: We describe the case of a 13-year-old male who presented with acute scrotal pain. We demonstrate how bedside ultrasound was used to make the diagnosis of testicular torsion, guide the technique for manual detorsion, and confirm adequate return of blood flow. Our case illustrates the ease with which POCUS can be used in real time to diagnose and treat organ-threatening pathology, but more importantly, it shows how real-time POCUS was used to detorse a testicle that was refractory to the standard detorsion technique. Conclusion: The acute scrotum is a time-sensitive presentation and if testicular torsion is present, the diagnosis should be made as soon as possible. Many Emergency Departments do not have 24-hour coverage of ultrasound technicians, which would delay the diagnosis and treatment. Moreover, when manual detorsion is attempted, it often does not work because the testicle may need more than the standard 180 degree medial to lateral rotation. POCUS provides real-time analysis of return of blood flow and can thus guide further rotation, or opposite direction rotation, as needed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.018
GPT teacher head0.294
Teacher spread0.275 · 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 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

Citations5
Published2021
Admission routes1
Has abstractyes

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