Quality improvement of lung cancer patient selection using clinic-based spirometry.
Bibliographic record
Abstract
282 Background: Coordinating lung cancer screening requires risk assessment for patient selection. Optimal selection can reduce costs and improve efficiency of low dose computed tomography (LDCT) screening of lung cancer. This study evaluated clinic-based spirometry as a tool to improve patient selection for lung cancer screening. Methods: Eligibility criteria for three large LDCT screening studies were retrospectively applied to the highest risk patients enrolled in the Princess Margaret Lung Cancer Screening Program who had received clinic-based research spirometry. The three studies were: Danish Lung Cancer Screening Trial (DLST), National Lung Screening Trial (NLST), and the Ontario Lung Cancer Screening Program (OLCS). Lung cancer incidence was compared between those who would were included by the screening study eligibility criteria (Group I), those who were excluded by the eligibility criteria but demonstrated obstruction on spirometry (defined as a Forced Expiratory Volume in 1 Second % Predicted (FEV1%) < 90%) (Group II), and those who did not meet eligibility criteria and had no obstruction (FEV1% ≥90) (Group III). Results: The 321 highest risk participants of the screening program had a mean age of 65 years and were 39% male. The median number of pack years in this group was 39. After undergoing spirometry, this cohort was screened using LDCT for a median of 3.3 years (range 1–8.1 years). Under DLST criteria, Groups I and II had virtually identical lung cancer incidences detected by screening at 13.1% and 13.6% of the individuals screened, respectively; Group III had a substantially lower incidence at 6.3%. Results were similar by NLST criteria where the incidence of screen-detected lung cancer were 13.7% for Groups I, 11.1% for Group II, and 8.6% for Group III. Under OLCS criteria, these values were 13.4% (Group I), 13.5% (Group II), and 8.2% (Group III). Conclusions: Individuals who were excluded from LDCT screening because they lacked other clinical eligibility criteria, but had a FEV1 < 90%, had similar lung cancer incidence as patients who had met screening study eligibility criteria. Coordinating care for screening of at-risk individuals could be improved by incorporating spirometric tools.
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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 | 0.032 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".