Practical implementation of noninvasive ventilation in Amyotrophic Lateral Sclerosis: lessons learned from a clinical case series
Bibliographic record
Abstract
PURPOSE: Noninvasive ventilation (NIV) may improve survival and quality of life in Amyotrophic Lateral Sclerosis (ALS) patients. There is a surprising paucity of practical guidelines for office-based implementation and management of NIV outside of tertiary ALS centers. We saw the need for a clinical protocol to allow feasible and consistent NIV management in this patient population. METHODS: We created a clinical protocol for office-based initiation of NIV implemented on consecutive ALS patients referred from our regional ALS multidisciplinary clinic. The protocol provided initial empiric settings using a bilevel device in volume-assured pressure support mode. A respiratory therapist (RT) initiated NIV in an office setting and made adjustments according to patient tolerance and therapy targets outlined in the protocol. Later setting changes were performed at patient or provider request. We evaluated patient adherence and efficacy via device download at 30 days and 1 year. RESULTS: We present data from a case series of the first 14 consecutive patients initiated on NIV over a 20-month period. Our protocol underwent iterative modification based on clinical experience and patient feedback. Early challenges included the significant time and resource burden required to coordinate device downloads and patient follow-up. Early 30-day NIV adherence was variable (median 20 out of 30 days), while 1-year NIV adherence was excellent (median 27.5 out of 30 days). CONCLUSIONS: Our RT-driven clinical NIV protocol was feasible but labor intensive. Achieving real-world adherence of NIV in our ALS patients required iterative protocol adjustment, significant RT provider time, and tele-based follow-up.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".