Traumatic spinal cord injuries – incidence, mechanisms and course
Bibliographic record
Abstract
BACKGROUND: The primary purpose of this article is to provide an overview of demography, neurological level of injury, extent of lesion, incidence, prevalence, injury mechanisms as well as lethality and causes of death associated with traumatic spinal cord injuries. MATERIAL AND METHOD: A literature search was carried out in PubMed, with the search words "traumatic spinal cord injury"/"traumatic spinal cord injuries" together with "epidemiology", and "spinal cord injury"/"spinal cord injuries" together with "epidemiology". RESULTS: The reported annual incidence of traumatic spinal cord injuries varies from 2.3 per million in a study from Canada to 83 per million in Alaska. The prevalence is given as ranging from 236 per million in India to 1800 per million in the USA. The average age at the time of injury varies from 26.8 years in Turkey to 55.5 years in the USA. The ratio of men to women varies from 0.9 in Taiwan to 12.0 in Nigeria. The most frequent cause of injury is traffic accidents, followed by falls, violence and sports/leisure activity incidents. Patients with traumatic spinal cord injuries have a higher lethality than the normal population. The most frequent causes of death today are airway problems, heart disease and suicide. INTERPRETATION: There are large geographical differences in reported incidence, prevalence and lethality. This is attributable to differences in definition, inclusion, classification and patient identification procedures in the various studies, together with geographical and cultural differences and differences in prehospital and hospital treatment.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".