Bibliographic record
Abstract
Background This study examined the influence of race/ethnicity on the individuals’ survival and neurological recovery within the first year after tSCI. Methods This retrospective cohort study included all 306 cases enrolled in the NASCIS-1, who were grouped into (a) African Americans (n=84), (b) non-Hispanic whites (n=159), and (c) other races/ethnicities that included Hispanics (n=60) and Asians (n=3). Outcome measures included survival and neurological recovery (as assessed using the NASCIS motor, and pinprick and light-touch sensory scores) within the first year post-tSCI. Data analyses of neurological recovery were adjusted for major potential confounders. Results There were 39 females and 267 males with a mean age of 31 years who mostly sustained cervical severe tSCI after vehicular accidents or falls. The three groups were comparable regarding sex distribution, level and severity of tSCI, level of consciousness at admission, and total received dose of methylprednisolone. However, African Americans were significantly older than non-Hispanic white individuals (P=0.0238). African Americans and individuals of other races/ethnicities had tSCI with open wounds caused by missile and water-related accidents more often than non-Hispanic white individuals (P<0.0001). However, survival rates within the first year post-tSCI were statistically comparable among the three groups (P=0.3191). Among the survivors, there was no statistically significant difference among the three groups regarding motor, and pinprick and light-touch sensory recovery (P>0.0500). Conclusions The results of this study suggest that the epidemiology of tSCI might vary depending upon the individual’s race/ethnicity. Nevertheless, race/ethnicity did not influence survival rate or neurological recovery within first year post-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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.844 | 0.762 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".