Home mechanical ventilation: A retrospective review of safety incidents using the World Health Organization International Patient Safety Event classification.
Bibliographic record
Abstract
BACKGROUND: There is a paucity of patient safety information from the community sector related to the medically fragile population requiring home mechanical ventilation (HMV). To improve safety, the risks HMV patients encounter must first be understood. OBJECTIVES: To describe patient safety incidents within the HMV population and discuss opportunities for preventing harm. METHODS: to quantitatively describe the types of incidents arising. Analysis of data classification was completed using descriptive and nonparametric statistics. RESULTS: Patient incidents were positive in 188 on-call logs; emerging from these were 227 incident types. Patient incident types included medical device issues (99 device failures, 41 user errors, 12 equipment availability), documentation (20 unavailable labels/prescriptions, four unclear information), clinical processes (16 inadequate treatment or general care) and clinical administration (10 inadequate handover or transfer of care). Patient incidents were associated with mild harm in 87 cases. CONCLUSIONS: The on-call logs were a good source of quality improvement data to understand harm and patient safety issues emerging in the HMV population. However, establishing a formal incident review and reporting system is required to provide a more comprehensive understanding.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".