Objective measurement of lung volume recruitment therapy: laboratory and clinical validation
Bibliographic record
Abstract
Lung volume recruitment manoeuvres are often prescribed to maintain respiratory health in neuromuscular disease. Unfortunately, no current system accurately records delivered dose. This study determined the performance characteristics of a novel, objective, manual lung volume recruitment bag counter (‘the counter’) with bench and healthy volunteer testing, as well as in individuals with neuromuscular disease. We undertook (1) bench test determination of activation threshold, (2) bench and healthy volunteer fidelity testing during simulated patient interface leak and different pressure compressions and (3) comparisons with self-report in individuals with neuromuscular disease. The data are reported as summary statistics, compression counts, percentage of recorded versus delivered compressions and concordance (Cohen’s kappa (K) and absolute agreement). Results Minimum counter activation pressure under conditions of zero leak was 1.9±0.4 cm H2O. No difference was observed between the number of repetitions delivered and recorded during high airway pressure condition. Interface leak approximating 25% resulted in underestimation of repetition counts, and once the leak was at 50% or beyond, the counter recorded no activity. Faster sampling frequency collected data with more fidelity. Counter data agreed with diary self-report during community trials (16 participants, 960 participant days, 77% agreement, Cohen’s Κ=0.66 and p<0.001). Disagreement typically favoured more diary reported (18%) than counter (5%) sessions. Conclusions The performance characteristics of a new lung volume recruitment counter have been established in both laboratory and community settings. Objective usage and dosage data should accelerate new knowledge development and better translation of lung volume recruitment therapy into policy and practice.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".