The fulcrum axis: an accurate measure of glenoid version on radiographs and computed tomography
Bibliographic record
Abstract
Background Proper glenoid position in total shoulder arthroplasty (TSA) is important. However, traditional glenoid version (GV) measurements overestimate retroversion on radiographs (XR) and computed tomography (CT). The fulcrum axis (FA) uses palpable surface landmarks and may be useful as an intra-operative guide. Also, the FA has not yet been validated on XR or CT in an arthritic population. Methods Four observers measured FA and GV on the XR, CT and three-dimensional CT (3DCT) of 40 patients who underwent TSA at a single institution from 2009 to 2015. Reliability and accuracy of FA and GV were calculated for XR and CT, using 3DCT as the gold standard. Results The mean FA and GV were 7.768° and 18.910° on XR; 6.23° and 12.920° on CT; and 8.100° and 7.740° on 3DCT, respectively. FA and GV were significantly different for XR and CT ( p < 0.001) but not for 3DCT ( p = 0.725). The inter-rater reliability, intra-rater reliability and accuracy of FA were not significantly different from GV and were 0.929 to 0.948, 0.779 to 0.974 and 0.674 to 0.705, respectively. However, the absolute difference of FA was closer to the gold standard (3DCT) than GV for XR (0.330° versus 11.172°) and CT (1.871° versus 5.178°) ( p < 0.001). Conclusions FA showed comparable reliability and accuracy to GV. However, FA more accurately reflected the gold standard.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".