5. Applying Data Mining Methods to Blast‐Related Mild Traumatic Brain Injury
Bibliographic record
Abstract
Mild traumatic brain injury (mTBI), or concussion, is one of the most common forms of injury sustained throughout Operations Iraqi Freedom and Enduring Freedom. Diagnosis is difficult for many of the symptoms are very common and may not manifest themselves immediately after the injury. Many studies on blast‐related mTBI have been published from research regarding the current war in Iraq and Afghanistan. Continuous work is underway to more accurately determine its causes and symptoms, as mTBI is considered difficult to diagnose. Using the Oak Ridge National Laboratory (ORNL) developed data mining software PIRANHA, an integrative literature analysis was conducted to assist and facilitate possible research on the processes of mTBI. Data were collected from academic databases as well as PIRANHA’s internet search function. The PIRANHA categories feature was employed to visualize the areas of overlapping research regarding manifestations of mTBI relating to the biological processes and behavioral changes. PIRANHA was also used to review current research on mTBI, including animal testing on mice and swine. The integrated results from these data mining analyses revealed areas that could be studied further, as well as a clearer indication of the specific processes of mTBI. These results can contribute to ORNL’s current LDRD, an mTBI‐specific project.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".