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Record W1991300865 · doi:10.1097/bte.0b013e3181e5d742

Humeral Head Posterior Subluxation on CT Scan: Validation and Comparison of 2 Methods of Measurement

2010· article· en· W1991300865 on OpenAlexaff
Jacob F. Kidder, Dominique M. Rouleau, Juan Pons‐Villanueva, Savvas Dynamidis, Michael J. DeFranco, Gilles Walch

Bibliographic record

VenueTechniques in Shoulder & Elbow Surgery · 2010
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsMedicineScapulaSubluxationIntraclass correlationReliability (semiconductor)Nuclear medicineOrthodonticsGlenoid cavityShoulder jointAnatomy

Abstract

fetched live from OpenAlex

Background Humeral static posterior translation is important for evaluation of osteoarthritis. Two different methods are compared for absolute difference and reliability. Methods A group of patients with shoulder pathology were analyzed. Images were evaluated 2 times with 2 methods by 3 evaluators. The first method, scapula method (SM), uses the scapula axis as a reference line (line drawn from the medial border of the scapula body to the center of the glenoid). The second method, named mediatrice method (MM), used the “mediatrice” line, drawn as a perpendicular line to glenoid joint surface passing in its middle. The percentage of the humeral head posterior to the line was assessed at the longest AP diameter. A percentage higher than 55% defined posterior subluxation. Reliability of both methods was obtained using consistency and interobserver agreement using intraclass correlation. Results One hundred fifteen cases met the inclusion criteria. The intraobserver reliability was very good using SM and good with MM. The interobserver reliability was very good for SM and good for MM. Conclusion The SM is slightly more reliable for subluxation measurement of the glenohumeral joint, however, both could be used to study the influence of humeral head subluxation on postoperative results.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.131
GPT teacher head0.453
Teacher spread0.322 · 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 designBench or experimental
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

Citations63
Published2010
Admission routes1
Has abstractyes

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