Identifying and classifying quality-of-life tools for assessing pressure ulcers after spinal cord injury
Bibliographic record
Abstract
CONTEXT: Although pressure ulcers may negatively influence quality of life (QoL) post-spinal cord injury (SCI), our understanding of how to assess their impact is confounded by conceptual and measurement issues. To ensure that descriptions of pressure ulcer impact are appropriately characterized, measures should be selected according to the domains that they evaluate and the population and pathologies for which they are designed. OBJECTIVE: To conduct a systematic literature review to identify and classify outcome measures used to assess the impact of pressure ulcers on QoL after SCI. METHODS: Electronic databases (Medline/PubMed, CINAHL, and PsycInfo) were searched for studies published between 1975 and 2011. Identified outcome measures were classified as being either subjective or objective using a QoL model. RESULTS: Fourteen studies were identified. The majority of tools identified in these studies did not have psychometric evidence supporting their use in the SCI population with the exception of two objective measures, the Short-Form 36 and the Craig Handicap Assessment and Reporting Technique, and two subjective measures, the Life Situation Questionnaire-Revised and the Ferrans and Powers Quality of Life Index SCI-Version. CONCLUSION: Many QoL outcome tools showed promise in being sensitive to the presence of pressure ulcers, but few of them have been validated for use with SCI. Prospective studies should employ more rigorous methods for collecting data on pressure ulcer severity and location to improve the quality of findings with regard to their impact on QoL. The Cardiff Wound Impact Schedule is a potential tool for assessing impact of pressure ulcers-post SCI.
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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 | 0.024 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".