International Spinal Cord Injury Fracture History Extended Data Set
Bibliographic record
Abstract
Objectives: The objective of the study is to develop the International Spinal Cord Injury (SCI) Fracture History Extended Data Set within the framework of the International SCI Data Sets to permit consistent collection and reporting of fracture history in the SCI population. Methods: The International SCI Fracture History Extended Data Set has been developed by a working group. The initial data set was open for 2 months for discussion and was revised based on suggestions from members of the International SCI Data Sets Committee, the International Spinal Cord Society (ISCoS) Executive and Scientific Committees, American Spinal Injury Association (ASIA) Board, other interested organizations, societies, and individual reviewers. The data set was also posted for 2 months for comments on ISCoS's and ASIA's websites. Results: The final data set contains questions on fractures after SCI. Because the information may be collected at any time, the date of data collection is important to capture relative to the time lapsed after SCI. The data set includes information on fracture history (location, etiology, treatment, complications for each fracture event), osteoporosis treatment (current and past use), bone measures by quantitative computed tomography (6 variables), and body composition (7 variables). The complete instructions for data collection and the data sheet itself are freely available on the ISCoS website (https://cdn.ymaws.com/www.iscos.org.uk/resource/resmgr/fracture/iscieds_fracture_1.pdf). Conclusion: The data set proposes to collect information on bone loss, other factors potentially predictive of fracture risk, and fracture in persons with SCI to guide clinical management and future research activities.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".