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Record W4396889140 · doi:10.1080/1091367x.2024.2347629

Inter-Evaluator Reliability of Sagittal and Rotational Spinal Measurements from 3D Ultrasound Imaging of Healthy Females in Standing with Varying Arm Positions

2024· article· en· W4396889140 on OpenAlexaff
Aislinn Ganci, Miran Qazizada, Brianna Fehr, Ana Vucenovic, Edmond Lou, Éric Parent

Bibliographic record

VenueMeasurement in Physical Education and Exercise Science · 2024
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsUniversity of Alberta
FundersScoliosis Research Society
KeywordsSagittal planeReliability (semiconductor)UltrasoundUltrasound imagingPhysical medicine and rehabilitationMedicineOrthodonticsNuclear medicinePhysical therapyAnatomyRadiologyPhysics

Abstract

fetched live from OpenAlex

Spinal alignment can be assessed without radiation using three-dimensional ultrasound imaging (3DUS). Reliable measurements could inform the ideal arm position for scoliosis radiographs. This study determined the inter-evaluator reliability of axial vertebral rotation (AVR) measurements and sagittal curve angles in healthy females from 3DUS spinal images in standing with two common radiograph arm positions: Chin and abduction (Abd). Three novice evaluators (R1, R2, and R3) measured images once. The Bland–Altman bias and limits of agreement (LOA) were calculated. T5-T12 kyphosis measurements met our bias and LOA error threshold only for R1 vs R2 in the Chin position [−0.53° (−10.7,9.7)]. The distance between the bias and the LOA also met our acceptable threshold of 5° for all mean AVRTwist, and all but one AVRTwist measurements (R1-R2_Chin = 5.9°). Three lordosis measurements did not meet our acceptable threshold (R1-R2_Chin, R1-R3_Chin, and R2-R3_Abd). The inter-evaluator reliability of novice evaluators was adequate and did not differ significantly between positions.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.063
GPT teacher head0.370
Teacher spread0.307 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

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