Evaluation of the<i>Patient Generated Index</i>as a measure of quality-of-life in people with severe traumatic brain injury
Bibliographic record
Abstract
OBJECTIVES: Quality-of-life (QoL) measures may be useful in quantifying the personal impact of an acquired brain injury and as an indicator of the effectiveness of service provision. This study investigated the validity of the patient generated index (PGI) as a measure of QoL with a sample of adults who had sustained a severe traumatic brain injury. METHOD: Fifty-five participants with an acquired brain injury were selected from four regional rehabilitation centres. Each participant completed validated questionnaires measuring subjective satisfaction with life, community integration, emotional distress, socially desirable responding and the patient generated index. RESULTS: The criterion-related validity of the PGI was demonstrated through a statistically significant positive correlation with life satisfaction. Evidence of construct validity was observed through significant correlations with measures of emotional distress and community integration. Depression had a strong negative association with QoL. Social desirability correlated significantly with both indices of QoL, suggesting that this variable may be treated as a covariate. CONCLUSION: The results offer some preliminary evidence for the validity of the PGI as a measure of QoL in people with severe brain injury.
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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 | 0.003 | 0.010 |
| 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 | 0.001 |
| 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".