Long-term mechanical ventilation and transitions in care: A narrative review
Bibliographic record
Abstract
OBJECTIVES: Individuals dependent on long-term mechanical ventilation (LTMV) for their day-to-day living are a heterogenous population who go through several transitions over their lifetime. This paper describes three transitions: 1) institution/hospital to community/home, 2) pediatric to adult care, and 3) active treatment to end-of-life for ventilator-assisted individuals (VAIs). METHODS: A narrative review based on literature and the author's collective practical and research experience. Four online databases were searched for relevant articles. A manual search for additional articles was completed and the results are summarized. RESULTS: Transitions from hospital to home, pediatric to adult care, and to end-of-life for VAIs are complex and challenging processes. Although there are several LTMV clinical practice guidelines highlighting key components for successful transition, there still exists gaps and inconsistencies in care. Most of the literature and experiences reported to date have been in developed countries or geographic areas with funded healthcare systems. CONCLUSIONS: For successful transitions, the VAIs and their support network must be front-and-center. There should be a coordinated, systematic, and holistic plan (including a multi-disciplinary team), life-time follow-up, with bespoke consideration of jurisdiction and individual circumstances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".