Changes in and Impact of the Death Review Process in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial
Bibliographic record
Abstract
Death review was conducted for the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer screening trial to avoid the biases associated with causes of death entered on death certificates. An algorithm selected deaths for review. Records on diagnosis and terminal illness were perused in the coordinating center and by the chair of the death review committee (DRC). Identifying information and randomization arm was removed. Three reviewers independently determined the cause of death. Disagreement was resolved at a meeting of the DRC. This process was subsequently simplified. The cause of death was determined by one DRC member and compared to the death certificate. With agreement the case was finalized. When discordant, the records were sent to a second DRC member. If the reviewers agreed, the case was finalized. If not, a third member reviewed. If two of the three reviewers agreed, the case was sent back to the discordant reviewer. If the reviewer remained discordant the case was resolved by a conference call. Of the 4728 death reviews that were completed, the DRC confirmed the death certificate underlying cause for over 90%. Between 5% and 13% of the certified deaths were regarded as indirect causes of death, associated with the treatment of the ascertained cancer; differential for prostate cancer, 11% in the intervention arm and 6% in the control. Without review, between 1% and 6% of the deaths that occurred would not have been assigned to the relevant PLCO cancer. The DRC completed 76% of those requiring review before the process ceased.
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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 | 0.144 | 0.383 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".