Computer International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) algorithms: a review
Bibliographic record
Abstract
STUDY DESIGN: Literature review and survey. OBJECTIVES: To provide an overview of existing computerized International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) algorithms and to evaluate the use of the current algorithms in research and clinical care. SETTING: Not applicable. METHODS: Literature review according to three organizing concepts for evaluation of Health Information Products (reach, usefulness, and use) was conducted. RESULTS: While the use of computerized ISNCSCI algorithms has been around for many years, many were developed and used internally for specific projects or not maintained. Today the International SCI community has free access to algorithms from the European Multicenter Study about Spinal Cord Injury (EMSCI) and the Praxis Spinal Cord Institute. Both algorithms have been validated in large datasets and are used in different SCI registries for quality control and education purposes. The use of the Praxis Institute algorithm by clinicians was highlighted through the Praxis User Survey (n = 76) which included participants from 27 countries. The survey found that over half of the participants using the algorithm (N = 69) did so on a regular basis (51%), with 54% having incorporated it into their regular workflow. CONCLUSIONS: Validated computerized ISNCSCI classification tools have evolved substantially and support education, clinical documentation, communication between clinicians and their patients, and ISNCSCI data quality around the world. They are not intended to replace well-trained clinicians, but allow for reclassification of ISNCSCI datasets with updated versions of the ISCNSCI, and support rapid classification of large datasets.
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 | 0.034 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.027 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".