Incidence of traumatic spinal cord injury worldwide: A systematic review, data integration, and update
Bibliographic record
Abstract
Objectives: This review was designed to update our earlier systematic review which evaluated both published and unpublished evidence on the incidence of traumatic spinal cord injury (TSCI) worldwide. Methods: We used various search methods including strategic searching, reference checking, searching for grey literature, contacting registries, authors, and organizations requesting unpublished data, browsing related websites, and hand searching key journals. The quality of included studies was evaluated by Joanna Briggs Institute Critical Appraisal Tools. Records published between April 2013 and May 2020 were added to the original systematic review. Results: Overall, 58 resources including 45 papers, 10 SCI registry reports, 1 book, and 2 theses were retrieved. We found TSCI incidence data for eight new countries, which overall shapes our knowledge of TSCI incidence for 49 countries. The incidence of TSCI ranges from 3.3 to 195.4 cases per million (cpm) based on subnational studies and from 5.1 to 150.48 cpm based on national studies. Most of the studies were low quality, lacked consistent case selection due to unclear definition of TSCI and unclear ascertainment methods. Conclusions: There is an increasing number of publications in the literature focusing on the epidemiologic data of TSCI. The absence of a standard form of reporting TSCI hinders the comparability of data across different data sources. Use of various definitions for TSCI may lead to heterogeneity in reports. Use of sensitivity analyses based on reasonable classification criteria can aid in offering a uniform set of case identification and ascertainment criteria for TSCI.
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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 | 0.029 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.039 | 0.036 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".