Diffusion Tensor Imaging Findings Are Not Strongly Associated With Postconcussional Disorder 2 Months Following Mild Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To examine the relation between diffusion tensor imaging (DTI) of the corpus callosum and postconcussion symptom reporting following mild traumatic brain injury (MTBI). PARTICIPANTS: Sixty patients with MTBI and 34 patients with orthopedic/soft-tissue injuries (Trauma Controls) prospectively enrolled from consecutive admissions to a level 1 trauma center. PROCEDURE: Diffusion tensor imaging of the corpus callosum was undertaken using a Phillips 3T scanner at 6 to 8 weeks postinjury. Participants also completed a postconcussion symptom checklist. The MTBI group was divided into 2 subgroups based on the International Classification of Diseases, Tenth Revision symptom criteria for postconcussion disorder (PCD): PCD Present (n = 21), PCD Absent (n = 39). MAIN OUTCOME MEASURES: Measures of fractional anisotropy and mean diffusivity for the genu, body, and splenium of the corpus callosum. Participants also completed the British Columbia Post-Concussion Symptom Inventory. RESULTS: The MTBI group reported more postconcussion symptoms than the trauma controls. There were no significant differences between MTBI and trauma control groups on all DTI measures. In the MTBI sample, there were no significant differences on all DTI measures between those who did and did not meet the International Classification of Diseases, Tenth Revision research criteria for postconcussion disorder. CONCLUSIONS: These data do not support an association between white matter integrity in the corpus callosum and self-reported postconcussion syndrome 6 to 8 weeks post-MTBI.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".