Using Single-Photon Emission Computerized Tomography on Patients With Positive Quantitative Electroencephalogram to Evaluate Chronic Mild Traumatic Brain Injury With Persistent Symptoms
Bibliographic record
Abstract
Background: Following mild traumatic brain injury (mTBI), also known as concussion, many patients with chronic symptoms (>3 months post injury) receive conventional imaging such as computed tomography (CT) or magnetic resonance imaging (MRI). However, these modalities often do not show changes after mTBI. We studied the benefit of triaging patients with ongoing symptoms >3 months post injury by quantitative electroencephalography (qEEG) and then completing a brain single positron emission computed tomography (SPECT) to aid in diagnosis and early detection of brain changes. Methods: We conducted a retrospective case review of 30 outpatients with mTBI. The patients were assessed by a neurologist, consented, and received a qEEG, and if the qEEG was positive, they consented and received a brain SPECT scan. The cases and diagnostic tools were collectively reviewed by a multidisciplinary group of physicians in biweekly team meetings including neurology, nuclear medicine, psychiatry, neuropsychiatry, general practice psychotherapy, neuro-ophthalmology, and chiropractic providers. The team noted the cause of injury, post injury symptoms, relevant past medical history, physical examination findings, and diagnoses, and commented on patients' SPECT scans. We then analyzed the SPECT scans quantitatively using the 3D-SSP software. Results: All the patients had cerebral perfusion abnormalities demonstrated by SPECT that were mostly undetectable by conventional imaging (CT/MRI). Perfusion changes were localized primarily in the cerebral cortex, basal ganglia, and cingulate cortex, and correlated with the patients' symptoms and examination findings. Qualitative and quantitative analyses yielded similar results. Most commonly, the patients experienced persistent headache, memory loss, concentration difficulties, depression, and cognitive impairment post mTBI. Because of their symptoms, most of the patients were unable to return to their previous employment and activity level. Conclusion: Our findings outline the physical basis of neurological and psychiatric symptoms experienced by patients with mTBI. Increased detection of mTBI can lead to development of improved targeted treatments for mTBI and its various sequelae.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".