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Record W4214826624 · doi:10.3389/fresc.2022.839819

Editorial: Advances in Musculoskeletal Ultrasound

2022· editorial· en· W4214826624 on OpenAlexaff
Wen‐Shiang Chen, Chueh‐Hung Wu, Mathieu Boudier‐Revéret

Bibliographic record

VenueFrontiers in Rehabilitation Sciences · 2022
Typeeditorial
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsRehabilitationMedicineMedical educationUltrasonographyCurriculumUltrasoundGlobePhysical therapyPhysical medicine and rehabilitationMedical physicsPsychologyRadiologyPedagogy

Abstract

fetched live from OpenAlex

In this research topic entitled "Advances in Musculoskeletal Ultrasound" published in Frontiers' 1 rehabilitation for musculoskeletal conditions section, we present to you four articles from authors 2 around the globe that illustrate the importance that ultrasound is taking in diverse rehabilitation 3 practices.As more and more residency programs involved in rehabilitation are incorporating ultrasound in 6 their curriculum, [1,2] and as many practicing rehabilitation professionals are getting trained in 7 both basic and advanced ultrasonography [3,4], the applications of this technology within our 8 field increase.The four articles in this go almost from head to toe; looking at the muscles of mastication and We hope readers will enjoy these four articles and that they will be stimulated to push even 46 further the use of ultrasound in rehabilitation for better patient care.

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)0.005
metaresearch head score (Gemma)0.021
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.027
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.021
Meta-epidemiology (narrow)0.0050.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0050.002
Science and technology studies0.0040.002
Scholarly communication0.0060.005
Open science0.0030.002
Research integrity0.0120.014
Insufficient payload (model declined to judge)0.0270.021

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 head0.005
GPT teacher head0.309
Teacher spread0.304 · 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
GenreEditorial

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

Citations0
Published2022
Admission routes1
Has abstractyes

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