Bibliographic record
Abstract
EOS is an ultra low dose 2D/3D digital Xray system developed by Biospace Med and a team of France - Quebec engineers and clinicians. EOS is based on the Nobel Prize for Physics winner Georges Charpak’s detector technology that has been adapted for clinical Xray imaging. It allows for the simultaneous acquisition of two perpendicular digital images of patient in a weight-bearing standing position. Using a stereoradiographic reconstruction scheme based on a priori geometrical and statistical information about the 3D bones, dedicated software allows for the reconstruction and dynamic visualization of the 3D bone surface of the spine, the pelvis, the femur, the tibia, etc. Once the 3D models are obtained, it is then possible to obtain automatically over 100 true 3D angle and length measurements (cobb angles, vertebral rotations, wedging, kyphosis, lordosis, pelvic parameters, pangonogram data). Software also allows making any 3D desired measurements, using a set of simple interactive tools. A dosimetric study done in our laboratories has shown a 6 to 9 fold decrease in dose received by the patients compared to a normal full-length spinal exam. Furthermore, applications are being developed to evaluate the 3D deformity pre-operatively and post-operatively. A recent retrospective study identified several 3D parameters distinguishing patients with significant curve progression when compared to a group of patients with no progression of their initial scoliotic curve at maturity. These findings are currently evaluated in a prospective study.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.012 |
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".