A tool to facilitate spirometry quality control assessment using 2019 standards
Bibliographic record
Abstract
Rationale: The 2019 ATS/ERS spirometry standards provide objective and subjective manoeuvre acceptability criteria. Subjective components may lead to discordance among expert reviewers. A tool using the 2019 criteria was developed to give reviewers a common approach to determine test acceptability and facilitate a decision consensus. Methods: The tool, which incorporates artificial intelligence quality assessment, applied the objective acceptability criteria and displayed key points (FEV1, end-expiration) on F-V and V-T graphs to aid reviewers in assessing contours and identifying anomalies. The tool evaluates and reports FEV1, FVC, BEV, plateau volume, expiratory time, rise time and inspired volume following EOFE (FIVC). Subjective assessments were: achievement of maximal flow and volume; occurrence of cough, glottis closure or any anomaly in 1st second of expiration; and glottis closure or early termination after 1 s of expiration. Reviewers complete a yes/no check box for each criterion. The tool calculates whether the manoeuvre is acceptable or usable. Results: Three reviewers used the tool to assess 150 tests. 50 were repeated to assess intra-rater reliability, (Kappa: 0.83, 0.86 and 0.80). All reviewers agreed on 63% of 100 unique tests (Kappa=0.5). Using the tool to develop reviewer consensus on each test, the individual agreement with the consensus was 88%, 94% and 70%. Conclusion: Despite using a set of strong objective criteria defined by the spirometry standards, intra- and inter-rater disagreement in assessing spirometry manoeuvre acceptability persists. The developed tool can be used as a powerful way to standardise assessment and reduce variability, and its additional benefits are being investigated
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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 | 0.117 | 0.312 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.021 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.018 |
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".