Latent Class Analysis of Brain Injury Symptomatology among College Students
Bibliographic record
Abstract
Background Incidents potentially causing mild brain injury (BI) are common, and most people recover rapidly; however, a subset experiences long-lasting challenges. Objective This study used latent class analysis to identify a subset of college students presenting chronic symptomatology consistent with a mild BI diagnosis and pseudo-class mean equality tests to examine relations between latent classes and BI event and academic outcome variables. Methods Participants were 118/423 undergraduates self-reporting possible mild BIs through a survey about general health, daily habits, academic performance, and potential BI events. Twenty-four cognitive, physiological, or socio-emotional sequelae served to identify symptomatology profiles. Results A three-class model including 11% with high symptomatology, 49% with moderate symptomatology, and 40% with negligible symptomatology provided excellent fit and entropy. Symptoms best separating high and moderate classes were memory, thinking speed, new learning, and attention problems. Mean equality tests revealed no significant difference in number of BI events across classes, but high symptomatology respondents were significantly less likely to lose consciousness and significantly more likely to have lower grade point averages and to have failed courses than moderate symptomatology respondents. Discussion Cognitive problems are paramount in distinguishing college students with chronic high symptomatology following BI from those with moderate and negligible symptomatology. Because high symptomatology class individuals differ academically from their counterparts, a functional consequence of mild BI appears to exist. Conclusion About 1 in 10 undergraduate students self-reporting BI events experiences chronic symptomatology affecting general health and academic achievement. Because they may benefit from supportive services, accurate identification is critical.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".