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Diagnostic accuracy of basic lung ultrasound in breathless patients over 60 years of age; stressing the protocol

2013· article· en· W2472254235 on OpenAlexaff
Kylie Baker, Geoffrey Mitchell, Angus Thompson, Geoffrey Stieler

Bibliographic record

VenueAustralasian Journal of Ultrasound in Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsSt. Paul's Hospital
FundersSpoedeisende Geneeskunde Onderzoeksfonds
KeywordsMedicineEmergency departmentLung ultrasoundAuditGold standard (test)Diagnostic accuracyEmergency medicineProtocol (science)Pulmonary diseaseLung diseaseLungUltrasoundRadiologyInternal medicinePathology

Abstract

fetched live from OpenAlex

Abstract Introduction : Emergency department differentiation of pulmonary oedema from chronic obstructive airways disease causing acute breathlessness is inaccurate 25% of the time despite clinical acumen, clinician‐reported chest x‐ray and ECG. This research investigates whether a basic lung ultrasound protocol (LUS) could improve identification of pulmonary oedema in breathless elderly patients. Method : Researchers prospectively sampled patients over 60 years, describing any breathlessness on presentation to a suburban emergency department. LUS studies were acquired by experienced or novice sonologists, interpreted by a blinded reviewer and compared with cardiologist chart audit for diagnosis at admission (gold standard). The admitting doctor's diagnosis, blinded to LUS, was compared with the chart audit result. Results : 204 LUS were collected, 145 by experienced sonologist and 59 by inexperienced. Diagnostic accuracy compared to cardiologist audit was 86.2% (95% CI 80.9 to 90.3), significantly higher than 70.2%, diagnostic accuracy for admission diagnosis, difference in proportion of 16% (95%CI 7.7 to 24.4%). Conclusion : A simple lung scanning protocol can help exclude pulmonary oedema in any breathless elderly 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)0.001
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient 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 score0.022
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.354
Teacher spread0.329 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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