Best practices for safe handling of products containing concentrated potassium
Bibliographic record
Abstract
Objective Post-traumatic headache (PTH) is one of the most frequent and persistent physical symptoms following mild traumatic brain injury (mTBI) and develop in more than 50% of this population. This study aimed to investigate the periaqueductal grey (PAG)-seeded functional connectivity (FC) in patients with mTBI with acute post-traumatic headache (APTH) and further examine whether the FC can be used as a neural biomarker to identify patients developing chronic pain 3 months postinjury. Methods 70 patients with mTBI underwent neuropsychological measurements and MRI scans within 7 days postinjury and 56 (80%) of patients were followed up at 3 months. 46 healthy controls completed the same protocol on recruitment to the study. PAG-seeded resting-state FC analysis was measured in 54 patients with mTBI with APTH, in comparison with 46 healthy volunteers. Results The mTBI+APTH group presented significantly reduced PAG-seeded FC within the default mode network (DMN), compared with healthy volunteers group. The connectivity strength can also predict patients’ complaints on the impact of headache on their lives. Crucially, the initial FC strength between the PAG-right precuneus as well as the PAG-right inferior parietal lobule became the important predictor to identify patients with mTBI developing persistent PTH 3 months postinjury. Conclusions Patients with mTBI+APTH exhibited significant PAG-related FC differences mainly within the DMN. These regions extended beyond traditional pain processing areas and may reflect the diminished top-down attention regulation of pain perception through antinociceptive descending modulation network. The disrupted PAG-DMN FC may be used as an early imaging biomarker to identify patients at risk of developing persistent PTH.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.045 |
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".