080 Home mechanical ventilation (HMV) in primary muscle disease: a 12-year single centre cohort study
Bibliographic record
Abstract
HMV support is required to manage ventilatory failure in different primary muscle disorders with varying patterns of respiratory muscle weakness and progression. We present a series of 232 patients with dystrophies who received HMV. The number of patients with the condition and the mean duration of support to death (n=75) or December 2019 was; Duchenne Muscular dystrophy – 113 patients (64 alive: 49 died) received HMV for 70m (1–240). Myotonic dystrophy – 42 patients (40:2) - 52m (1–185). Limb girdle – 36 patients (18:18) – 71m (4–130). Congenital – 14 (all alive) – 50m (6–101). Facioscapulohumeral – 9 (8:1) – 94m (46–209) Becker – 8 (3:5) – 40m (15–120) Ventilation was generally supported with a NIPPY 3+ device using pressure control mode with varying dependence and interfaces according to disease severity and patient preference. 57% patients had established respiratory failure, 9% underwent tracheostomy, 51% used cough assist devices and 31% underwent gastrostomy. This series indicates that prolonged survival is possible for patients with primary muscle disease who develop respiratory muscle weakness. The ventilation method, the level of home support and the need for adjuvant support over long periods may vary according to the wishes of the patient, the extent of generalized limb and bulbar weakness, and the pattern of progression. robinhoward@nhs.net
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".