The Prevalence of Adrenal Insufficiency in Individuals with Traumatic Spinal Cord Injury: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background/Objectives: Spinal cord injury (SCI) causes profound autonomic and endocrine dysfunctions, giving rise to adrenal insufficiency (AI), which is marked by a reduction in steroid hormone production. Left unaddressed, SCI-related AI (SCI-AI) can lead to life-threatening consequences such as severe hypotension and shock (i.e., adrenal crisis). However, symptoms are often non-specific, making AI challenging to distinguish from similar or overlapping cardiovascular conditions (e.g., orthostatic hypotension). Additionally, the etiology of SCI-AI remains unknown. This review aimed to synthesize the current literature reporting the prevalence, symptomology, and management of SCI-AI. Methods: A systematic search was performed to identify studies reporting AI following the cessation of glucocorticoid treatments in individuals with traumatic SCI. A random-effects meta-analysis was conducted to investigate the overall prevalence of SCI-AI. Results: Thirteen studies involving 545 individuals with traumatic SCI, most with cervical level injuries (n = 256), met the review criteria. A total of 4 studies were included in the meta-analysis. Primary analysis results indicated an SCI-AI pooled prevalence of 24.3% (event rate [ER] = 0.243, 95% confidence interval [CI] = 0.073–0.565, n = 4). Additional sensitivity analyses showed a pooled prevalence of 46.3% (ER = 0.463, 95%CI = 0.348–0.582, n = 2) and 10.8% (ER = 0.108, 95%CI = 0.025–0.368, n = 2) for case–control and retrospective cohort studies, respectively. High-dose glucocorticoid administration after SCI as well as the injury itself appear to contribute to the development of AI. Conclusions: The estimated prevalence of AI in people with traumatic SCI was high (24%). Prevalence was also greater among individuals with cervical SCI than those with lower-level lesions. Clinicians should be vigilant in recognizing the symptomatology and onset of SCI-AI. Further research elucidating its underlying pathophysiology is needed to optimize glucocorticoid administration for remediating AI in this vulnerable population.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".