Variation Among Spirometry Interpretation Algorithms
Bibliographic record
Abstract
Several algorithms exist to facilitate spirometric interpretation in clinical practice, yet there is a lack of consensus on how spirometric criteria for asthma, COPD, and restrictive disorders should be incorporated into spirometry interpretation algorithms suitable for use in day-to-day primary care management. The purpose of this review was to identify and describe the variability that exists among spirometry interpretation algorithms and how this might be relevant to the interpretation of spirometric data of common conditions encountered in primary care. MEDLINE, Embase, and mainstream search engines were used to identify all English-language spirometry interpretation algorithm–related material between January 1990 and December 2018. Eight variations in spirometry interpretation algorithms were identified via specific a priori assumptions that each spirometry interpretation algorithm should contain content consistent with national and international guidelines related to spirometry interpretation. Of the 26 spirometry interpretation algorithms identified, 5 were deemed impractical for day-to-day use in primary care (19%), 23 lacked a logic string leading to the postbronchodilator FEV 1 /FVC (88%), 4 relied on postbronchodilator change in FEV 1 to distinguish between asthma and COPD (15%), 24 lacked a prompt for bronchodilator challenge when FEV 1 /FVC was considered to be at a normal level (92%), 12 did not indicate whether the data represented a prebronchodilator or postbronchodilator scenario (46%), 7 did not include a logic string that considers mixed obstructive/restrictive defect (27%), 23 did not contain a prompt to refer for methacholine challenge testing when spirometry appeared normal (88%), and 2 spirometry interpretation algorithms did not include a logic string leading to restrictive disorder (8%). Our review suggests that there is considerable variability among spirometry interpretation algorithms available as diagnostic aids and that there is a need for standardization of spirometry interpretation algorithms in primary care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".