The Canadian Journal of CME / July 2004 95 Focus on CME at
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.767 | 0.483 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".