Оцінка змін якості життя комбатантів із легкою черепно-мозковою травмою
Bibliographic record
Abstract
Objective — to assess the quality of life of combatants with mild traumatic brain injury (TBI) and to determine the relationships between impairments in physical and mental health (Physical and Mental Component Summary scores of the MOS SF-36) and the presence of increased anxiety, depression, sleep disturbances, and cognitive impairment. Materials and methods. The study involved 20 combatants with mild traumatic brain injury (TBI). Patients were assessed using the following instruments: the MOS SF-36 (Medical Outcomes Study 36-Item Short Form Health Survey) for general assessment of quality of life across key domains; HADS (Hospital Anxiety and Depression Scale) to assess anxiety and depression levels; PSQI (Pittsburgh Sleep Quality Index) for comprehensive evaluation of sleep disturbances; and MoCA (Montreal Cognitive Assessment) to assess cognitive impairment, including mild cognitive disorders. Results and discussion. Median questionnaire scores were as follows: Physical Component Summary of the MOS SF-36 — 41.8 % (34.1—52.9); Mental Component Summary — 31.5 % (24.3—37.2); HADS-A — 13 (8.5—15.0); HADS-D — 11 (6.0—14.5); MoCA — 24 (17—26); PSQI — 12 (8—16). Correlation analysis was performed to examine associations between MOS SF-36 scores and levels of anxiety (HADS-A), depression (HADS-D), sleep disturbances (PSQI), and cognitive impairment (MoCA). A strong negative correlation was identified between the Mental Component Summary of the MOS SF-36 and anxiety levels (ρ = –0.78, p < 0.05), indicating that higher anxiety was associated with poorer quality of life. Conclusions. The findings indicate that elevated anxiety levels in combatants with mild TBI significantly affect quality of life. Therefore, anxiety symptoms should be appropriately addressed and managed in this patient population.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".