Managing Traumatic Brain Injury: Appropriate Assessment and a Rationale for Using Neurofeedback and Biofeedback to Enhance Recovery in Postconcussion Syndrome
Bibliographic record
Abstract
Impairments that may result from a mild traumatic brain injury (TBI) or concussion can be both severe and long-lasting. This article will list some of the common persisting symptoms that may occur and give a brief description of the neuropathological processes that can be triggered by TBI, including diffuse axonal injury and its effects on the mitochondrial Kreb's cycle and the production of adenosine triphosphate, the brain's source of energy. This is followed by a summary of a comprehensive assessment process that includes quantitative electroencephalography, evoked potentials, heart rate variability (HRV) measures, neuropsychological testing, and blood and urine analysis. Details concerning a neurophysiological approach to effective treatment are given. These include conventional single-channel neurofeedback (NFB), also called brain-computer interface training, low-resolution electromagnetic tomography z-score neurofeedback, HRV training, and counseling on diet, sleep, and exercise. The authors expand the discussion on their treatment approach to include a neuroanatomical explanation of why the practitioner should consider combining the NFB training with HRV training.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".