Review of Awakening Agents
Bibliographic record
Abstract
Brain injuries are a serious burden of illness to Canada and the US. Advances in managing head trauma have allowed more patients to emerge from decreased levels of consciousness and helped them cope with neurocognitive, neurobehavioural, and neuropsychiatric deficits. In this article, we review the current (1986-2002) evidence surrounding the pharmacological management of arousal states and the aforementioned neurological sequelae of head injury in either acute or chronic conditions. This article will review the evidence for the use of psychostimulants (methylphenidate), antidepressants (amitriptyline, selective serotonin reuptake inhibitors, and buproprion), Parkinson's medications (amantadine, bromocriptine, carbidopa/levodopa), anticonvulsants (valproic acid), modafinil (Provigil), lactate, hyperbaric oxygen chamber, electroconvulsive therapy, and transmagnetic stimulation, in patients following a head injury. The review did not include all anticonvulsants, neuroleptics, beta-blockers, benzodiazepines, azospirones or cognitive enhancers. Unfortunately, the quality of the evidence is generally poor, and sometimes conflicting, which in turn results in indecisive guidelines for treating patients. Accepting the inherent flaws in the evidence we feel that this paper may serve as a stepping-stone for future researchers to improve data gathering that targets neurocognitive, neurobehavioural and neuropsychiatric symptoms following a head injury.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".