Long-term Home Mechanical Ventilation of Children in İstanbul
Bibliographic record
Abstract
OBJECTIVE:The aims of this multi-center study were to describe the characteristics of children receiving long-term home mechanical ventilation (HMV) in İstanbul and to compare the patients receiving non-invasive and invasive ventilation. MATERIAL AND METHODS:This cross-sectional multicenter study included all children receiving long-term HMV followed by admission to six tertiary hospitals.The data were collected between May 2020 and May 2021.Demographic data and data regarding HMV were collected from the patient charts. RESULTS:The study included 416 participants.The most common diagnoses were neuromuscular (35.1%) and neurological diseases (25.7%).Among the patients, 49.5% (n = 206) received non-invasive ventilation (NIV), whereas 50.5% (n = 210) received invasive ventilation.The median age at initiation was significantly younger in the invasive ventilation group than in the NIV group (10 vs. 41 months, P < 0.001).Most subjects in the NIV group (81.1%) received ventilation support only during sleep, whereas most subjects in the invasive ventilation group (55.7%) received continuous ventilator support (P < 0.001).In addition to ventilation support, 41.9% of the subjects in the invasive ventilation group and 28.6% in the NIV group received oxygen supplementation (P = 0.002).Within the last year, 59.1% (n = 246) of the subjects were hospitalized.The risk factors for hospitalization were invasive ventilation, continuous ventilatory support, oxygen supplementation, tube feeding, and swallowing dysfunction (P = 0.002, 0.009, <0.001, <0.001 and <0.001 respectively).CONCLUSION: Despite the increasing use of NIV in most studies, half of the study population received invasive ventilation.Patients receiving invasive ventilation were more likely to require continuous ventilator support and oxygen supplementation and were at increased risk of hospitalization.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".