Hyperhomocysteinemia: A Risk Factor for Cognitive Decline in mTBI Patients
Bibliographic record
Abstract
Objective Our quest was to find an answer for “Why are some people able to recover 100% from a concussion/traumatic brain injury while others tend to have prolonged symptoms after their concussion/traumatic brain injury?” Background The prevalence of hyperhomocysteinemia in general is 5%–7% with increasing evidence showing higher prevalence of HHcy as age increases. The prevalence of vitamin D deficiency was 41.6% in the American population. The prevalence of vitamin B-12 deficiency is at least 40% in the patients of the Americas. With recent data, the prevalence of magnesium deficiency is around 10%–30% of the population. Hyperhomocysteinemia due MTHFR gene mutation B-12, B-6, magnesium, and folic acid deficiency is well established. Design/Methods A retrospective study involving 45 patients was conducted in order to correlate the persistent symptoms concussion head injury/traumatic brain injury and their bio nutraceutical deficiency. Results This data provides evidence that a patient9s Homocysteine levels are significantly linearly related with their MoCA scores (t = −5.837, df = 34, p-value = 1.403e-06, [95% CI: −0.8406114 to −0.4936554]). In the mTBI group, for every 1 umol/L increase in Homocysteine levels, there is a 0.54217 decrease in MoCA scores. mTBI patients that had Homocysteine levels greater than 14 umol/L were 76% more likely to experience cognitive decline. The mean MoCA score of mTBI patients is significantly lower than the mean MoCA score of patients in the control group (t = −3.2898, df = 67, p-value = 0.0016, [95% CI: −6.710893 to −1.642642]). The mean Homocysteine levels of mTBI patients are significantly greater than the mean Homocysteine levels of patients in the control group (t = 2.2182, df = 85, p-value = 0.0292, [95% CI: 0.3039847 to 5.5603010]). Conclusions mTBI patients should be routinely screened for serum homocysteine, vitamin D, B12, B6 and magnesium levels to know their risk for cognitive decline.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".