Inferring quality of life from performance-based assessments
Bibliographic record
Abstract
PURPOSE: Performance based measures have been suggested as an approach to estimate quality of life, but the associations have not been extensively evaluated. This study's purpose was to determine the associations between quality of life and performance based assessments of disablements in community dwelling individuals post-stroke. METHODS: Forty five people were evaluated in a cross-sectional pilot study. The subjects' quality of life (SF-36), ability in basic and instrumental activities of daily living (ADL), manual dexterity, mobility, neurological impairment, and perception of lateral neglect were evaluated. Multiple regression was employed to find the strongest associations. RESULTS: Neurological impairment explained 34% of the variation in the women's physical health summary score (PCS) of the SF-36. Instrumental ADL and neurological impairment together explained 66% of the women's mental health summary score (MCS) of the SF-36. Manual dexterity of the hemiplegic hand explained 39% of the variation in the men's PCS. CONCLUSION: Performance based measures may be useful to estimate quality of life in non-communicative individuals.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".