Spirometry interpretation for primary care: A validation study
Bibliographic record
Abstract
Objective measurement of lung function with spirometry should be done in primary care to allow immediate feedback on diagnosis and management of respiratory conditions. Lack of training necessitates a teaching program and tool for spirometric interpretation. The Family Physician Airways Group of Canada (www.fpagc.com) has taught this at a five primary care conferences over 3 years. This post program survey reviewed participants’ attitudes to this spirometry algorithm. 121/138 surveys were returned. 40% of the participants had been in practice for less than 10 years and 31% more than 20 years. Most were in a group practice. 63% were currently not doing spirometry, yet they came to learn interpretative skills and were debating the purchase of a machine. Spirometry was annually ordered less than 10 times by 19%, 10-50 times by 46% and more than 100 times by 16%. 21% of physicians were currently using an algorithm tool. 71% of participants said that they would now use the tool provided in the program. 98% of participants found the tool helpful in interpreting spirometry, with 93% of physicians saying that they will use this in their practice in the future. They felt the algorithm would assist them in diagnosing asthma (91%), assessing asthma control (94%) and diagnosing COPD (94%). Barriers related to the performance of spirometry in primary care; time, cost, technique, monetary reimbursement and interpretation of grey zone cases. The creation and teaching of a spirometry program and interpretation algorithm tool has successfully created comfort in spirometry interpretation in most participants. This was a particularly strongly motivated group of learners, as had to pay to attend this specially accredited program.
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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 | 0.027 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".