Did death certificates and a death review process agree on lung cancer cause of death in the National Lung Screening Trial?
Bibliographic record
Abstract
BACKGROUND/AIMS: Randomized controlled trials frequently use death review committees to assign a cause of death rather than relying on cause of death information from death certificates. The National Lung Screening Trial, a randomized controlled trial of lung cancer screening with low-dose computed tomography versus chest X-ray for heavy and/or long-term smokers ages 55-74 years at enrollment, used a committee blinded to arm assignment for a subset of deaths to determine whether cause of death was due to lung cancer. METHODS: Deaths were selected for review using a pre-determined computerized algorithm. The algorithm, which considered cancers diagnosed during the trial, causes and significant conditions listed on the death certificate, and the underlying cause of death derived from death certificate information by trained nosologists, selected deaths that were most likely to represent a death due to lung cancer (either directly or indirectly) and deaths that might have been erroneously assigned lung cancer as the cause of death. The algorithm also selected deaths that might be due to adverse events of diagnostic evaluation for lung cancer. Using the review cause of death as the gold standard and lung cancer cause of death as the outcome of interest (dichotomized as lung cancer versus not lung cancer), we calculated performance measures of the death certificate cause of death. We also recalculated the trial primary endpoint using the death certificate cause of death. RESULTS: In all, 1642 deaths were reviewed and assigned a cause of death (42% of the 3877 National Lung Screening Trial deaths). Sensitivity of death certificate cause of death was 91%; specificity, 97%; positive predictive value, 98%; and negative predictive value, 89%. About 40% of the deaths reclassified to lung cancer cause of death had a death certificate cause of death of a neoplasm other than lung. Using the death certificate cause of death, the lung cancer mortality reduction was 18% (95% confidence interval: 4.2-25.0), as compared with the published finding of 20% (95% confidence interval: 6.7-26.7). CONCLUSION: Death review may not be necessary for primary-outcome analyses in lung cancer screening trials. If deemed necessary, researchers should strive to streamline the death review process as much as possible.
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 | 0.781 | 0.862 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".