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Record W3175625279 · doi:10.22374/cjgim.v16i2.507

Minimal Criteria for Lung Ultrasonography in Internal Medicine

2021· article· en· W3175625279 on OpenAlexaffvenue
Janeve Desy, Vicki E. Noble, Andrew S. Liteplo, Paul Olszynski, Brian Buchanan, Renee K. Dversdal, Shane Arishenkoff, Gigi Liu, Elaine Dumoulin, Irene Ma

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsUniversity of British ColumbiaUniversity of AlbertaUniversity of SaskatchewanUniversity of Calgary
Fundersnot available
KeywordsMedicinePrimary careUltrasonographyGynecologySurgeryFamily medicine

Abstract

fetched live from OpenAlex

BackgroundPoint-of-care lung ultrasound (LUS) examination is increasingly utilized in Internal Medicine. To improve the standardization of LUS education and clinical use, explicit minimal criteria for defining what is an acceptable and clinically useful image are needed.MethodsA 97-item online survey of potential minimal criteria for common uses of LUS in Internal Medicine was developed and sent to 10 international point-of-care ultrasound experts. Their opinion on the inclusion of each item was sought and items not achieving consensus (defined as agreement by at least 70% of the experts) were reassessed in subsequent rounds. A total of three rounds were conducted.ResultsSeventy-four minimal criteria were agreed upon for inclusion, 24 were agreed upon for exclusion, and two did not reach consensus.ConclusionsExperts agreed on 74 minimal criteria for Internal Medicine LUS. The use of these minimal criteria during teaching and clinical use is strongly recommended. RésuméContexteL’échographie pulmonaire au point d’intervention est de plus en plus utilisée en médecine interne. Pour améliorer l’uniformisation de la formation sur l’échographie pulmonaire et de son utilisation clinique, il faut des critères minimaux explicites pour définir ce qu’est une image acceptable et utile sur le plan clinique.MéthodologieUn sondage en ligne de 97 éléments portant sur des critères minimaux possibles dans l’utilisation courante de l’échographie pulmonaire en médecine interne a été élaboré et soumis à 10 experts internationaux en échographie au point d’intervention. Leur avis sur l’inclusion de chaque élément a été sondé, et les éléments pour lesquels il n’y avait pas de consensus (défini par l’accord d’au moins 70 % des experts) ont été réévalués lors de tours suivants. Au total, trois tours ont été effectués.RésultatsSoixante-quatorze critères minimaux ont été acceptés, 24 ont été exclus et deux n’ont pas fait consensus.ConclusionsLes experts se sont entendus sur 74 critères minimaux relatifs à l’échographie pulmonaire en médecine interne. L’utilisation de ces critères minimaux au cours de l’enseignement et de l’utilisation clinique est fortement recommandée.

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)0.039
metaresearch head score (Gemma)0.148
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.039
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.148
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.003
Science and technology studies0.0020.005
Scholarly communication0.0030.003
Open science0.0030.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0080.002

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 head0.051
GPT teacher head0.381
Teacher spread0.330 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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".

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Citations7
Published2021
Admission routes2
Has abstractyes

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