Non-invasive ventilation in palliative care: a systematic review
Bibliographic record
Abstract
INTRODUCTION: An ageing population and steady increase in the rates of neoplasms and chronic degenerative diseases poses a challenge for societies and their healthcare systems. Because of the recent and continued advances in therapies, such as the development and widespread use of non-invasive ventilation (NIV), survival rates have increased for these pathologies. For patients with end-stage chronic respiratory diseases, the use of NIV following the onset of acute or severe chronic respiratory failure is a valid option when intubation has been excluded. EVIDENCE ACQUISITION: The following electronic databases were searched from their inception to January 2000 to December 2017: MEDLINE, EMBASE, CINHAIL, CENTRAL (Cochrane Central register of Controlled Trials), DARE (Database of Abstracts of Reviews of Effectiveness), the Cochrane Database of Systematic Reviews, ACP Journal Club database. EVIDENCE SYNTHESIS: The available evidence strongly supports the use of NIV in patients presenting with an exacerbation of chronic obstructive pulmonary disease, as well end-stage neuromuscular disease. Few studies support the use of NIV in end-stage interstitial lung disease and in morbid obesity patients. In patients with cancer has been recommend offering NIV as palliative care to improve dyspnea. CONCLUSIONS: The decision regarding the treatment should be made by the patient, ideally before reaching the terminal stage and after having a frank dialogue with healthcare professionals and family members.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| 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) | 0.006 | 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".