Bibliographic record
Abstract
SUMMARY: There is currently no evidence that preventive treatment of unruptured aneurysms or AVMs is beneficial and randomized trials have been proposed to address this clinical uncertainty. Participation in a trial may necessitate a shift of point of view compared to a certain habitual clinical mentality. A review of the ethical and rational principles governing the design and realization of a trial may help integrate clinical research into expert clinical practices. The treatment of unruptured aneurysms and AVMs remains controversial, and data from observational studies cannot provide a normative basis for clinical decisions. Prevention targets healthy individuals and hence has an obligation of results. There is no opposition between the search for objective facts using scientific methods and the ethics of medical practice since a good practice cannot forbid physicians the means to define what could be beneficial to patients. Perhaps the most difficult task is to recognize the uncertainty that is crucial to allow resorting to trial methodology. The reasoning that is used in research and analysis differs from the casuistic methods typical of clinical work, but clinical judgement remains the dominant factor that decides both who enters the trial and to whom the results of the trial will apply. Randomization is still perceived as a difficult and strange method to integrate into normal practice, but in the face of uncertainty it assures the best chances for the best outcome to each participant. Some tension exists between scientific methods and normal practice, but they need to coexist if we are to progress at the same time we care for patients.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".