Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Study Design Survey of 100 worldwide spine surgeons. Objective To develop a spine injury score for the AOSpine Thoracolumbar Spine Injury Classification System. Methods Each respondent was asked to numerically grade the severity of each variable of the AOSpine Thoracolumbar Spine Injury Classification System. Using the results, as well as limited input from the AOSpine Trauma Knowledge Forum, the Thoracolumbar AOSpine Injury Score was developed. Results Beginning with 1 point for A1, groups A, B, and C were consecutively awarded an additional point (A1, 1 point; A2, 2 points; A3, 3 points); however, because of a significant increase in the severity between A3 and A4 and because the severity of A4 and B1 was similar, both A4 and B1 were awarded 5 points. An uneven stepwise increase in severity moving from N0 to N4, with a substantial increase in severity between N2 (nerve root injury with radicular symptoms) and N3 (incomplete spinal cord injury) injuries, was identified. Hence, each grade of neurologic injury was progressively given an additional point starting with 0 points for N0, and the substantial difference in severity between N2 and N3 injuries was recognized by elevating N3 to 4 points. Finally, 1 point was awarded to the M1 modifier (indeterminate posterolateral ligamentous complex injury). Conclusion The Thoracolumbar AOSpine Injury Score is an easy-to-use, data-driven metric that will allow for the development of a surgical algorithm to accompany the AOSpine Thoracolumbar Spine Injury Classification System.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it