Calcium and Non-Penetrating Traumatic Brain Injury: A Proposal for the Implementation of an Early Therapeutic Treatment for Initial Head Insults
Bibliographic record
Abstract
Finding an effective treatment for traumatic brain injury is challenging for multiple reasons. There are innumerable different causes and resulting levels of damage for both penetrating and non-penetrating traumatic brain injury each of which shows diverse pathophysiological progressions. More concerning is that disease progression can take decades before neurological symptoms become obvious. Currently, the primary treatment for non-penetrating mild traumatic brain injury, also called concussion, is bed rest despite the fact the majority of emergency room visits for traumatic brain injury are due to this mild form. Furthermore, one-third of mild traumatic brain injury cases progress to long-term serious symptoms. This argues for the earliest therapeutic intervention for all mild traumatic brain injury cases which is the focus of this review. Calcium levels are greatly increased in damaged brain regions as a result of the initial impact due to tissue damage as well as disrupted ion channels. The dysregulated calcium level feedback is a diversity of ways to further augment calcium neurotoxicity. This suggests that targeting calcium levels and function would be a strong therapeutic approach. An effective calcium-based traumatic brain injury therapy could best be developed through therapeutic programs organized in professional team sports where mild traumatic brain injury events are common, large numbers of subjects are involved and professional personnel are available to oversee treatment and documentation. This review concludes with a proposal with that focus.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".