Subjective Improvement following Treatment with Carbamazepin (Tegretol) for a Subpopulation of Patients with Traumatic Brain Injuries
Bibliographic record
Abstract
Over a 3-yr period, 19 patients who had sustained brain traumas during motor vehicle incidents and who exhibited abnormal scores for a dichotic word-listening task and Roberts' Epileptic Spectrum Disorder Inventory more than one year after the injury were recommended for treatment with carbamazepine (Tegretol). The psychiatric profile of these patients, as defined by the Minnesota Multiphasic Personality Inventory, was similar to the profile of patients from other studies who had displayed more objective improvement following this treatment. Of the 14 patients 12 who followed the recommendation retrospectively reported that within a few weeks after treatment they experienced marked reductions in the incidence of sudden confusion and depression, increased attention and focus, and either elimination or attenuation of an aversive sensed presence. Such results suggest that many of the debilitating symptoms that persist for months to years after a traumatic brain injury may be electrical in nature rather than due to "psychological responses" and might be treatable by appropriate dosages of carbamazepine or other, e.g., Gabapentin (Neurontin) antiepileptic compounds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".