Current synthetic overview on spinal cord injury epidemiological data
Bibliographic record
Abstract
Aim. To have updated information on the epidemiology of spinal cord injury (SCI) is required for developing an adequate and effective related health policy strategies and consequent contextual decisions making regarding this category of patients and also for planning and implementing SCI prevention education and measures. Accordingly, the rationale of this article is to provide a systematic overview of the literature regarding SCI epidemiology. Material and methods. We reviewed epidemiological published reports and searched on internet specifically databases, from different centres, worldwide, about SCI, collecting descriptive data for properly estimating the incidence, prevalence, and/ or causes of SCI. Results. The global annual incidence rate is considered to be 23 cases of Traumatic Spinal Cord Injury (TSCI) per million (179,312 new cases per annum – results provided by World Health Organisation’s (WHO) in 2007). Prevalence per million inhabitants varies quite largely among statistics in different countries (from 280 in Finland to 681 in Australia, 755 in the United States of America or maybe even more, and even bigger in Canada). Men more commonly suffer from this kind of pathology and the direction of SCI evolution is to have a higher cord lesion level (more tetraplegics than paraplegics) and age at injury. Conclusion. Even if the results of this literature review showed that the SCI incidence and prevalence are rising, they did not suffer significant changes in the last three decades of time. The prevalence surveys remain poor, mainly because a basic requirement for having correct and appropriately updated figures would need national and or regional electronic dedicated registers of evidence, and this is not a situation frequent enough. But the incidence studies from USA and Europe have been increased in the last years. This article asserts the need for improving the SCI data standardised collection in many countries, especially in the ones from low developed or emergent areas.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.042 | 0.037 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".