ERS statement on paediatric long-term noninvasive respiratory support
Bibliographic record
Abstract
Long-term noninvasive respiratory support, comprising continuous positive airway pressure (CPAP) and noninvasive ventilation (NIV), in children is expanding worldwide, with increasing complexities of children being considered for this type of ventilator support and expanding indications such as palliative care. There have been improvements in equipment and interfaces. Despite growing experience, there are still gaps in a significant number of areas: there is a lack of validated criteria for CPAP/NIV initiation, optimal follow-up and monitoring; weaning and long-term benefits have not been evaluated. Therapeutic education of the caregivers and the patient is of paramount importance, as well as continuous support and assistance, in order to achieve optimal adherence. The preservation or improvement of the quality of life of the patient and caregivers should be a concern for all children treated with long-term CPAP/NIV. As NIV is a highly specialised treatment, patients are usually managed by an experienced paediatric multidisciplinary team. This statement written by experts in the field of paediatric long-term CPAP/NIV aims to emphasise the most recent scientific input and should open up new perspectives and research areas.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".