Economic Issues in the Use of Office Spirometry for Lung Health Assessment
Bibliographic record
Abstract
The National Lung Health Education Program (United States) has recently recommended using office spirometry to screen for subclinical lung disease in adult smokers. No published studies evaluate the economic consequences of this recommendation. This review article outlines the issues that must be considered when evaluating the costs and health benefits of office spirometry. Much of the available data on the effectiveness of screening is from studies that included smoking cessation interventions, making it difficult to determine the effects of screening alone. The sensitivity and specificity of screening spirometry are not known, but may not be important in the economic model, because even false positive test results are beneficial if they lead to smoking cessation. Costs to be considered include those of spirometry itself, of implementing and maintaining screening and smoking cessation programs, and of their consequences, ie, reduced morbidity (lower short term health care costs) and mortality (perhaps higher long term health care costs). Despite these unique challenges, data are available to perform economic analyses regarding screening spirometry. Such analyses should play a role in future clinical policy making. Even modest quit rates attributable to screening spirometry may result in highly favourable cost effectiveness ratios.
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 | 0.071 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".