Population based randomised controlled trial on impact of screening on mortality from abdominal aortic aneurysm
Bibliographic record
Abstract
Abstract Objective To assess whether screening for abdominal aortic aneurysms in men reduces mortality. Design Population based randomised controlled trial of ultrasound screening, with intention to treat analysis of age standardised mortality. Setting Community based screening programme in Western Australia. Participants 41 000 men aged 65-83 years randomised to intervention and control groups. Intervention Invitation to ultrasound screening. Main outcome measure Deaths from abdominal aortic aneurysm in the five years after the start of screening. Results The corrected response to invitation to screening was 70%. The crude prevalence was 7.2% for aortic diameter ≥ 30 mm and 0.5% for diameter ≥ 55 mm. Twice as many men in the intervention group than in the control group underwent elective surgery for abdominal aortic aneurysm (107 v 54, P = 0.002, χ 2 test). Between scheduled screening and the end of follow up 18 men in the intervention group and 25 in the control group died from abdominal aortic aneurysm, yielding a mortality ratio of 0.61 (95% confidence interval 0.33 to 1.11). Any benefit was almost entirely in men aged between 65 and 75 years, where the ratio was reduced to 0.19 (0.04 to 0.89). Conclusions At a whole population level screening for abdominal aortic aneurysms was not effective in men aged 65-83 years and did not reduce overall death rates. The success of screening depends on choice of target age group and the exclusion of ineligible men. It is also important to assess the current rate of elective surgery for abdominal aortic aneurysm as in some communities this may already approach a level that reduces the potential benefit of population based screening.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".