Excel Macro for Lung Volume Recruitment Counter Data (Omega Data Logger).
Bibliographic record
Abstract
Lung volume recruitment (LVR) is a common respiratory therapy for people living with restrictive neuromuscular disease. LVR is important in acute respiratory compromise, during respiratory rehabilitation and as a preventative therapy. Cohort data associate LVR use with increased survival is diseases such as Duchenne Muscular Dystrophy.1 While LVR is a commonly prescribed therapy, we have previously had to rely on self-report alone for any indication of adherence with prescribed therapy. This has significantly hampered both clinical decision making and research into effectiveness. In the paper from Canada and Australia that these data support, two large clinical services have collaborated to develop an objective LVR counter, to undertake extensive bench-testing, physiological testing, and to assess counter performance compared with diary self-report. The clinical validation was undertaken within a large, single-centre randomized controlled trial. The counter performed well and we believe the innovation will impact clinical care and research. These instructions, example output, and macro-enabled Excel spreadsheet are contined herein. 1. McKim DA, Katz SL, Barrowman N, et al. Lung Volume Recruitment Slows Pulmonary Function Decline in Duchenne Muscular Dystrophy. Archives of Physical Medicine and Rehabilitation 2012;93(7):1117-22.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.253 | 0.095 |
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".