The Effect of Age and Pain on Quantitative Sensory Testing Measurements After Moderate-to-Severe Traumatic Brain Injury: Preliminary Findings
Bibliographic record
Abstract
BACKGROUND: Chronic pain after moderate-to-severe traumatic brain injury (TBI) is associated with notable sensory alterations. Although the incidence of TBI is rapidly growing in older populations, elderly individuals have been largely excluded from sensory testing studies, thus limiting evidence regarding the influence of age on pain-related sensory alterations after TBI. This study aimed to investigate the effect of age on the sensory profiles of patients with and without chronic pain after moderate-to-severe TBI. METHODS: Thermal and mechanical quantitative sensory testing were performed on the painful and contralateral body regions in TBI participants with pain (TBI-P) and on both forearms in TBI participants without pain (TBI-NP). Descriptive information about chronic pain and psychological comorbidities was assessed using validated questionnaires. RESULTS: Participants included 37 young (18-59 years, 57% with chronic pain) and 22 elderly (≥60 years, 46% with chronic pain) survivors of moderate-to-severe TBI. TBI-P participants exhibited significant alterations in heat and pressure pain sensitivity compared to TBI-NP participants, with more pronounced decreases in heat detection in the elderly group and increased warmth sensitivity in the young group. Alterations were not always associated with chronic pain, as cold hypoesthesia was found in elderly TBI-NP participants. In both age groups, chronic pain was associated with higher levels of depressive mood. CONCLUSIONS: Results suggest that young and elderly TBI survivors have both common and unique sensory properties, highlighting the need to pursue sensory testing studies in older patient groups. Depression might also be an important target for pain management after TBI.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".