Is there any gender or age-related discrepancy in the waiting time for each step in the surgical management of acute traumatic cervical spinal cord injury?
Bibliographic record
Abstract
Context/Objective: Prior studies indicate that patient’s gender and age can influence treatment choices during spine disease management. This study examines whether individual’s gender and age at injury onset influence the waiting time for each step in the surgical management of patients with acute traumatic cervical spinal cord injury (atcSCI).Design: Retrospective cohort study.Setting: Quaternary spine trauma center.Participants: This study included consecutive individuals with atcSCI admitted from August/2002 to October/2008 who were enrolled in the Surgical Trial in Acute Spinal Cord Injury Study (STASCIS).Interventions: Spinal cord decompression.Outcome Measures: Data on the periods of time for each step in the surgical management were analyzed to explore the potential effects of gender and age at injury onset.Results: There were 64 individuals with atcSCI (17 women, 47 men; age range: 18–78 years; mean age: 50.5 ± 2.1 years). Older age was associated with longer stay in the acute spine center, but this association was cofounded by major pre-existing medical co-morbidities. Age did not significantly affect the waiting time for each step in the surgical management of these individuals with atcSCI. Women underwent surgical assessment earlier than men. Gender did not influence other key steps in the surgical management.Conclusion: The study results suggest that older age at injury onset was associated with longer stay in the acute spine care center, and women had a shorter waiting time for surgical assessment than men. Nevertheless, no other age or gender bias was identified in the waiting times for the steps in the management of atcSCI.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 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".