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Record W2030403761 · doi:10.1177/1742271x13486265

My Patient is Short of Breath: Is the Problem in the Lung Tissue?

2013· article· en· W2030403761 on OpenAlexaff
J. Milne, Paul Atkinson, Justin Bowra, Osama Loubani, Andrew Smith

Bibliographic record

VenueUltrasound · 2013
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsMemorial University of NewfoundlandSaint John Regional HospitalDalhousie University
Fundersnot available
KeywordsMedicinePresentation (obstetrics)Emergency departmentPoint of care ultrasoundRadiologyMedical physicsParenchymaLungPhysical examinationIntensive care medicineRadiographyUltrasoundPathologyNursingInternal medicine

Abstract

fetched live from OpenAlex

Undifferentiated dyspnoea is a common patient presentation in the intensive care unit, medical and surgical floors, and in the emergency department. Physical examination and chest radiography are notoriously insensitive for detection and differentiation of various lung pathologies while computed tomography consumes significant resources and exposes the patient to ionizing radiation. Point-of-care ultrasound (PoCUS) is a highly sensitive and specific diagnostic tool that, with appropriate operator experience, is capable of diagnosing and differentiating between the various causes of dyspnoea. PoCUS machines are readily available, images are rapidly generated and repeatable, and technical skills are easily taught during short training sessions. Furthermore, the development of PoCUS skills in one specific area enables and enhances the development of skills in other non-related areas. This article describes the benefits, technical aspects, and challenges associated with using PoCUS to examine the lung parenchyma in the acutely dyspnoeic patient.

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 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 score0.189
Threshold uncertainty score0.999

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)0.0020.000

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 head0.023
GPT teacher head0.318
Teacher spread0.295 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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