Development of a quality assessment algorithm for multiple breath nitrogen washout
Bibliographic record
Abstract
Introduction: There is a need to integrate quality control (QC) into multiple breath washout (MBW) tests to facilitate accurate interpretation of results. We aimed to develop an algorithm to automate application of QC criteria for MBW. Methods: We defined quantitative measures of quality into a series of algorithms [Jensen R. et al. PLoS ONE; 2016] and applied these to MBW data collected on the Exhalyzer® D (EcoMedics AG). The performance of the algorithm was compared to independent assessment by experienced reviewers. Results: Anonymized MBW data (15,070 trials; 2,866 tests) in healthy children, and subjects with Cystic Fibrosis, were retrospectively evaluated using the algorithm. Seventy percent of trials were categorized as accept/exclude, whereas 30% were flagged for manual review. The algorithm agreed with the reviewer’s decision for 88% of trials. The lung clearance index was similar between the algorithm and reviewers (Δ -0.01, limits of agreement -0.31, 0.29) and resulted in a similar proportion of acceptable test occasions (86.4% vs. 83.6%). An equal number of trials flagged for review were subsequently classified as accept/exclude by reviewers. Conclusion: QC criteria can successfully be applied to MBW data using an algorithm and only requires review in approximately 30% of trials. The agreement between the algorithm and manual review was similar to previously observed inter-reviewer agreement [Jensen R. et al. PLoS ONE; 2016]; disagreement may reflect subjective interpretation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.099 | 0.248 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".