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Record W2594545627 · doi:10.3138/ptc.2016-38

Influence of Ultrasound Transducer Tilt in the Cranial and Caudal Directions on Measurements of Inter-Rectus Distance in Parous Women

2017· article· en· W2594545627 on OpenAlexaffvenue
Nicole F. Hills, Nadia Keshwani, Linda McLean

Bibliographic record

VenuePhysiotherapy Canada · 2017
Typearticle
Languageen
FieldMedicine
TopicPregnancy-related medical research
Canadian institutionsUniversity of OttawaQueen's University
Fundersnot available
KeywordsTransducerUltrasoundMedicineAnatomyLift (data mining)Tilt (camera)PerpendicularOrthodonticsNuclear medicineAcousticsRadiologyMathematicsPhysicsComputer scienceGeometry

Abstract

fetched live from OpenAlex

Purpose: An increased inter-rectus distance (IRD) can persist after a pregnancy and may be associated with lumbopelvic dysfunction. Ultrasound imaging (USI) is currently the gold standard for measuring IRD; however, no study has explored the need to standardize the transducer angle during these evaluations. The purpose of this study was to determine whether the angle of the ultrasound transducer relative to the underlying abdominal wall has an effect on measurements of IRD in parous women. Method: Ultrasound images of the linea alba (LA) were captured from 15 women, at rest and during a head lift, beginning with images acquired perpendicular to the LA at the midline, then tilted in 5° increments to 15° in both the cranial and the caudal directions. Repeated-measures analyses of variance were used to test for systematic differences in IRD measurements among the transducer angles in both the rest and the head-lift conditions. An α of 0.05 was used for all tests. Results: No significant effect of transducer angle was found in IRD measurements acquired with participants at rest (F 2.24,31.3 =1.814; p=0.18) or during a head lift (F 3.15,44.1 =1.315; p=0.28). Conclusion: When using USI, cranial or caudal tilt errors in transducer angle do not appear to pose a problem when measuring IRD.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.572

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)0.0000.000

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 head0.020
GPT teacher head0.324
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2017
Admission routes2
Has abstractyes

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