Quality of life after traumatic brain injury: a cross-sectional analysis uncovers age- and sex-related differences over the adult life span
Bibliographic record
Abstract
Traumatic brain injury (TBI) is the leading cause of disability in the working population and becomes increasingly prevalent in the elderly. Thus, TBI is a major global health burden. However, age- and sex-related long-term outcome regarding patient's health-related quality of life (HRQoL) is yet not clarified. In this cross-sectional study, we present age- and sex-related demographics and HRQoL up to 10 years after TBI using the Quality of Life after Brain Injury (QOLIBRI) instrument. The QOLIBRI total score ranges from zero to 100 indicating good (≥ 60), moderate (40-59) or unfavorable (< 40) HRQoL. Two-thirds of the entire chronic TBI cohort (102 males; 33 females) aged 18-85 years reported good HRQoL up to 10 years after TBI. TBI etiology differed between sexes with females suffering more often from traffic- than fall-related TBI (p = 0.01) with increasing prevalence during aging (p = < 0.001). HRQoL (good/moderate/unfavorable) differed between sexes (p < 0.0001) with 17% more females reporting moderate outcome (p = 0.01). Specifically, older females (54-76-years at TBI) were affected, while males constantly reported good HRQoL (p = 0.017). Cognition (p = 0.014), self-perception (p = 0.009), and emotions (p = 0.016) rather than physical problems (p = 0.1) constrained older females' HRQoL after TBI. Experiencing TBI during aging does not influence HRQoL outcome in males but females suggesting that female brains cope less well with a traumatic injury during aging. Therefore, older females need long-term follow-ups after TBI to detect neuropsychiatric sequels that restrict their quality of life. Further investigations are necessary to uncover the mechanisms of this so far unknown phenomenon.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".