Analysis of Discourse Production to Assess Cognitive Communication Deficits Following Mild Traumatic Brain Injury With and Without Posttraumatic Stress
Bibliographic record
Abstract
Purpose: Cognitive communication deficits can be difficult to assess in individuals with mild traumatic brain injury (mTBI). However, the use of discourse analysis as a direct and sensitive metric of cognitive communication skills has shown promising clinical utility for other TBI severity levels. This exploratory study investigated discourse production in service members and veterans (SMVs) with uncomplicated mTBI with and without posttraumatic stress disorder (PTSD) and SMVs with neither mTBI or PTSD. Method: Fifteen SMVs with mTBI and PTSD, 26 with mTBI, and 25 controls with no brain injury (NBI) and without PTSD were given a wordless picture story to elicit spontaneous discourse. Discourse samples were analyzed for global coherence, word count, the use of negative emotion words, cognitive process words, nonfluencies, and story completeness. Results: Results revealed a significant difference between the mTBI ( Mdn = 3.33) and NBI ( Mdn = 3.50) groups, χ 2 (3) = 6.044, p = .017, ε 2 = .03, for global coherence. Word count differed significantly between the mTBI + PTSD ( Mdn = 135) and NBI ( Mdn = 195) groups, χ 2 (3) = 7.968, p = .006, ε 2 = .06. No other group differences were observed. Discussion: Structural features of discourse production may serve as potential markers of cognitive communication deficits in mTBI. Furthermore, PTSD may contribute to verbal fluency deficits in individuals with mTBI. Additional research is needed to develop discourse-related measures that are more sensitive to the effects of mTBI and PTSD.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".