Expert-driven weighting of pressure injury risk factors for wheelchair users: A Delphi study
Bibliographic record
Abstract
ObjectiveTo identify and prioritize personal risk factors for pressure injuries in wheelchair users.DesignA Delphi survey was conducted with clinicians specializing in pressure injury prevention and care.SettingThe study was conducted online using LimeSurvey software.ParticipantsIn the first round, 90 clinicians participated and completed the survey; in the second round, 68 continued their involvement.ResultsAcross all rounds, 39 risk factors were identified by the experts. These factors were weighted according to expert consensus. Immobility, current or past pressure injuries, malnutrition, and sensory perception impairment are ranked among the highest important.ConclusionThese findings underscore the importance of considering both physiological and behavioral factors when assessing pressure injurie risk. The weighted list of expert-validated factors offers clinicians a practical foundation for more targeted and individualized prevention strategies, ultimately supporting improved care and quality of life for this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".