Home non-invasive ventilation (NIV) : Patients cognitive performance and skills at setup
Bibliographic record
Abstract
Rationale: Patients need to acquire specific skills for the use of home NIV. No study as yet described the cognitive performance of patients at NIV setup. There is a lack of data of patients9 skills on their use of NIV. Aim: To assess the cognitive performance and patients9 skills at NIV setup and their consequences in adherence. Methods: Prospective audit conducted in Lane Fox Respiratory Unit, London from 02/2015 and 12/2015 including all patients admitted for home NIV setup and expected to be independent in its use. Assessments were: Montreal Cognitive Assessment (MOCA), Instrumental Activities of Daily Living (IADL), education level and visual analogue scales to assess patients own understanding and skills. They were performed at NIV setup and at 6 weeks follow-up. Results: 104 patients completed follow-up. Mean age was 62±15 years-old, mean BMI was 35±11kgm/2. Underlying disease was obesity hypoventilation syndrome (n:37), COPD-OSA (n:28), neuromuscular/chest wall diseases (n:15), COPD (n:13), OSA (n:11). At baseline: MOCA was 22.3±0.5 and IADL was 5.7±2.2. 45 (43%) patients left school at 16 years-old. Patients9 understandings and self-perceived skills are summarised in figure 1. At follow-up, mean use of NIV was 4.5±3.4 hours/day and MOCA improved by 2 (p<0.001). Conclusion: Patients admitted for NIV setup have low cognitive performance and independence level. NIV training should be adjusted to patient9s cognitive level.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".