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Record W2055110130 · doi:10.1007/s11999-007-0089-z

CT Scan Method Accurately Assesses Humeral Head Retroversion

2008· article· en· W2055110130 on OpenAlexaff
Pascal Boileau, Ryan T. Bicknell, Noel Mazzoleni, Gilles Walch, J.-P. Urien

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2008
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsKingston General HospitalQueen's University
Fundersnot available
KeywordsMedicineCadaveric spasmHumerusAnatomyRadiographyCadaverHead (geology)OrthodonticsNuclear medicineRadiologyGeology

Abstract

fetched live from OpenAlex

Humeral head retroversion is not well described with the literature controversial regarding accuracy of measurement methods and ranges of normal values. We therefore determined normal humeral head retroversion and assessed the measurement methods. We measured retroversion in 65 cadaveric humeri, including 52 paired specimens, using four methods: radiographic, computed tomography (CT) scan, computer-assisted, and direct methods. We also assessed the distance between the humeral head central axis and the bicipital groove. CT scan methods accurately measure humeral head retroversion, while radiographic methods do not. The retroversion with respect to the transepicondylar axis was 17.9 degrees and 21.5 degrees with respect to the trochlear tangent axis. The difference between the right and left humeri was 8.9 degrees. The distance between the central axis of the humeral head and the bicipital groove was 7.0 mm and was consistent between right and left humeri. Humeral head retroversion may be most accurately obtained using the patient's own anatomic landmarks or, if not, identifiable retroversion as measured by those landmarks on contralateral side or the bicipital groove.

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)0.004
metaresearch head score (Gemma)0.002
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 score0.118
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.376
GPT teacher head0.572
Teacher spread0.196 · 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

Citations94
Published2008
Admission routes1
Has abstractyes

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