Considerable variation exists among spirometry interpretation algorithms
Bibliographic record
Abstract
Background: Several algorithms exist to facilitate spirometric interpretation in clinical practice; yet, there is a lack of consensus on how spirometric criteria for asthma and COPD diagnoses should be incorporated into spirometry interpretation algorithms (SIA) suitable for use in day-to-day primary care management of these conditions. Purpose: To identify and describe the variability among SIA and how this might be relevant to the interpretation of spirometric data and management of asthma and COPD in primary care. Methods: Medline, Embase and mainstream search engines identified all English-language SIA related material between January 1990 to December 2018. Seven variations in SIA were identified via specific a-priori assumptions that each SIA should contain content consistent with guideline recommendations. Results: Of the 26 SIA that met inclusion criteria, 5 (19%) were deemed impractical for day-to-day use in primary care, 23 (88%) lacked a logic string leading to the post-bronchodilator FEV1/FVC ratio, 4 (15%) relied on post-bronchodilator change in FEV1 to distinguish between asthma and COPD, 24 (94%) lacked a prompt for bronchodilator challenge when FEV1/FVC was considered to be at a normal level, 12 (46%) did not indicate whether the data represented a pre- or post-bronchodilator scenario, 7 (27%) did not include a logic string that considers mixed obstructive/restrictive defect and 23 (88%) did not contain a prompt to refer for MCT when spirometry appeared to be normal. Conclusion: Our findings describe considerable variability among SIA available for adoption as diagnostic aids in primary care and highlight the need to develop standards for SIA suitable for clinical 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 | 0.100 | 0.321 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".