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Record W4367184307 · doi:10.24908/pocus.v8i1.15831

Sonographic Features of a Tuberculous Cold Abscess: A Case Report and Literature Review

2023· article· en· W4367184307 on OpenAlexvenueno aff
Amir Alzarrad, Salman Bin Naeem, Serena Rovida

Bibliographic record

VenuePOCUS Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicInfectious Diseases and Tuberculosis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePalpationExtrapulmonary tuberculosisTuberculosisPhysical examinationRadiologyConcomitantChest painAbscessSurgeryPathologyMycobacterium tuberculosis

Abstract

fetched live from OpenAlex

The use of point of care ultrasound (POCUS) to aid diagnosis of tuberculosis has been investigated in countries where concomitant endemic prevalence of HIV increases the incidence of extrapulmonary tuberculosis (EPTB). In such cases, using a focused assessment with sonography for HIV-associated tuberculosis (FASH) scan has found to be immensely advantageous as a rapid diagnostic tool in low resource settings where other imaging modalities are scarce. The prevalence of EPTB in immunocompetent patients in industrialised countries is growing. Since EPTB can manifest itself in almost any part of the human body, symptomatic patients present with constitutional and non-specific symptoms. In our case, a 44-year-old male presented to the emergency department (ED) with a 3-month history of left-sided chest pain and swelling of the chest wall. Clinical examination revealed a swollen and tender lump above the left first rib. Palpation of the thoracic (T7) vertebral body demonstrated localised pain. POCUS showed a collection of heterogenous material with fluid content and specks of hyperechoic 'ring-like' structures. Further investigations led to the diagnosis of EPTB. The patient was admitted and treated for EPTB where he went on to make a full recovery. This case report highlights the role of integrating POCUS in clinical examination of patients with suspected EPTB, which can expedite its diagnosis and management.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.290
Teacher spread0.281 · 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 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

Citations4
Published2023
Admission routes1
Has abstractyes

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