Imaging Chronic Traumatic Encephalopathy: A Biomedical Engineering Perspective
Bibliographic record
Abstract
A disease initially associated with boxers ninety years ago, chronic traumatic encephalopathy (CTE) is now recognized as a significant risk to boxers, American football players, ice hockey players, military personnel or anyone to whom recurrent head injuries are a distinct possibility. Diagnosis is currently confirmed at autopsy, although CTE's presumed sufferers have symptoms of depression, suicidal thoughts, mood and personality changes, and loss of memory. CTE sufferers also complain of losing cognitive ability, dysfunction in everyday activities, inability to keep regular employment, violent tendencies and marital strife. Dementia may develop over the long term. Unfortunately, there is no clear consensus in regards to pathology, with both number and severity of head injuries being linked to disease progression. Despite the slow advancement of this disease, there are no clinical methods to diagnose or monitor prognosis in presumed patients, limiting clinicians' efforts to symptom management. The lack of diagnostic tools fuels the need for biomedical engineers to develop techniques for in vivo detection of CTE. This review examines efforts made with various magnetic resonance and nuclear imaging techniques, with a view towards improving the sensitivity and specificity of diagnostic imaging for CTE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".