Understanding decision making for preventive interventions: The unruptured intracranial aneurysm example
Bibliographic record
Abstract
BACKGROUND: Decision making for preventive interventions in asymptomatic patients, such as the treatment of incidental intracranial aneurysms, is eminently uncertain and at risk of over-treatment. One approach suggests that the weighing of the natural risk of the disease against the risk of intervention should be replaced by a comparison of outcomes measured as expected quality-adjusted life-years survival. METHODS: We review the problems of over-diagnosis and over-treatment and how prognostic studies can help address the clinical uncertainty. We examine and compare the assumptions that underlie the mathematical transformations that are involved in the so-called outcome-based approach with the risk-based approach when they are both derived from observational data. Finally, we propose a more pragmatic approach. RESULTS: Both risk-based and outcome-based models depend on two strong assumptions: exchangeability of patients selected to be observed and patients selected to be treated (in other words ignorability of treatment assignment), and ii) dominance of time-to-event data (the only thing pertinent for decision making is the time to the first event in the patient's history). The outcome-based approach needs an additional assumption: fatality (once a patient suffers a poor outcome from an event, recovery is impossible). These three theoretical assumptions are rarely verified in practice. CONCLUSION: Clinical decision-making based on observational data relies on unrealistic assumptions. Clinical practice should instead be guided by conducting pragmatic clinical trials.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".