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Record W2461998791 · doi:10.1007/s40134-016-0169-5

Economics of Musculoskeletal Ultrasound

2016· review· en· W2461998791 on OpenAlexafffund
Nathalie J. Bureau, Daniela Ziegler

Bibliographic record

VenueCurrent Radiology Reports · 2016
Typereview
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersNational Institutes of HealthSiemens CanadaNational Institute for Health and Care ResearchHealth Research Board
KeywordsMedicineUltrasoundRadiologyMedical physics

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Healthcare costs have exploded in the past 30 years and they are a major concern for governments worldwide. Care management of musculoskeletal disorders and advanced imaging account for a large part of this socioeconomic burden. RECENT FINDINGS: Musculoskeletal ultrasound is now performed primarily by nonradiologists. Both musculoskeletal ultrasound and MRI total utilization rates continue to increase. Despite the existence of evidence-based diagnostic recommendations and the potential cost-savings of using musculoskeletal ultrasound instead of MRI in certain clinical situations, ensuring appropriate use of imaging among health professionals remains difficult for various reasons. SUMMARY: In the context of healthcare budgets restraints, use of imaging must be shown scientifically, to improve patient outcomes and be cost-effective. Current evidence recommends musculoskeletal ultrasound as the primary imaging modality in the investigation of rotator cuff disease. Policies aiming at ensuring the application of imaging guidelines among physicians are 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 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)0.003
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.005
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0200.002

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 head0.054
GPT teacher head0.396
Teacher spread0.343 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations47
Published2016
Admission routes2
Has abstractyes

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