Oral Presentation Abstracts from the ASIA 38th Annual Scientific Meeting; Denver, Colorado; April 19-21, 2012
Bibliographic record
Abstract
Objective: To develop a minimal data set to describe spinal column injuries, referred to as the International SCI Spinal Column Injury Basic Data Set.Design: Expert opinion, feedback, and final consensus.Participants/ Methods: An expert committee defined the data elements included in the International SCI Spinal Column Injury Basic Data Set.The data set was then disseminated to the appropriate committees and organizations for comment.All feedback was considered, and the final version was endorsed by both the International Spinal Cord Society and the American Spinal Injury Association.Results: The data set consists of 7 variables: (1) penetrating/blunt injury, (2) spinal column injury(ies), (3) single/multiple level spinal column injury(ies), (4) spinal column injury level number, (5) spinal column injury level, (6) disc/ posterior ligamentous complex injury, and (7) traumatic translation.All variables are coded using numbers or characters.Each spinal column injury is coded (variable 4) and described (variables 5-7).Sample clinical cases will be presented to illustrate how the data are coded.Conclusion: The International SCI Spinal Column Injury Basic Data Set will facilitate comparisons of spinal column injury data among studies and countries.It is part of the National Institute of Neurological Disorders and Stroke Common Data Element project and can now be included in SCI clinical studies.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.275 | 0.100 |
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".