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Record W7098290975

The Canadian Journal of CME / July 2004 95 Focus on CME at

2015· article· en· W7098290975 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhytochemical Studies and Bioactivities
Canadian institutionsnot available
Fundersnot available
KeywordsRotator cuffTearsAsymptomaticCuffGreater TuberosityMagnetic resonance imaging
DOInot available

Abstract

fetched live from OpenAlex

A: The rotator cuff is comprised of four mus-cles, each having different movements and strengths. The tendons of these muscles come together prior to their insertion in the region of the greater tuberosity of the humerus. The rotator cuff plays a key role in sta-bilizing the glenohumeral joint during shoulder movements. The muscles func-tion along nearly all the axes of movement with varying intensity and the stabilization provided by the cuff minimizes transla-tion, or sliding movements. The rotator cuff can be torn partially or completely. • Partial tear: Occurs when some of the cuff’s fibres or layers are torn on the joint side, bursal side, or even within the ten-dons; the whole being characteristic of tendinosis. • Complete tear: Occurs when all the cuff layers are affected. While the resulting significant structural loss gen-erally involves the supraspinous ten-don, it may also involve one, two, or even three tendons. Tears range from small to massive. It is surprising that many patients with structural damage to the cuff are entirely asymptomatic. In a stdy of asymptomatic volunetters aged 50 to 59, magnetic resonance revealed a 23 % prevalence of par-tial or complete tear of the rotator cuff.2 In fact, one study showed a 54 % prevalence of rotator cuff tear among patients over 60.1 Even today, we do not know exactly why one person with a torn rotator cuff will suffer, while someone else with a similar tear has no symptoms whatsoever. One study showed 54 % of patientsover 60 had a rotator cuff tear.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.935

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 head0.018
GPT teacher head0.231
Teacher spread0.214 · 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 designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

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