Abstracts of the 32nd Brain Impairment Conference, 7–9 May, 2009, Sydney
Bibliographic record
Abstract
I t has been widely reported that individuals with traumatic brain injury (TBI) may develop psychiatric problems post-injury.However there is considerable variability in reported rates of disorders, which reflects significant methodological differences in studies, in terms of measures used, recruitment methods, participants' injury severity, time since injury and documentation of pre-injury psychiatric and substance use disorders.Few studies have examined a range of disorders, most having focused on depression.In this workshop, results of both retrospective and prospective studies will be presented, which have aimed to establish the frequency of Axis 1 psychiatric disorders pre-and post-TBI, in participants up to 5 years post-injury, predictors of post-injury psychiatric disorders including pre-injury disorders, and their association with functional outcome.Results of a second prospective study examining alcohol and drug use in the first three years after injury, relative to pre-injury consumption and in comparison with a demographically similar control group will also be presented.Factors associated with postinjury alcohol and drug use will be examined.The findings from these studies highlight the need for interventions to alleviate anxiety, depression and alcohol use after injury.Preliminary findings from intervention studies aimed at addressing these problems following TBI will be presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.214 | 0.084 |
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".