Bibliographic record
Abstract
The history of human opinion is scarcely anything more than the history of human error. Voltaire1 The knowledge vacuum surrounding the management of unruptured intracranial aneurysms (UIAs) persists. It is acknowledged that good quality (randomized) data on which to base clinical decisions do not exist. However, this has not stopped the manufacture of non-evidence-based devices used to justify approaches where clinical decisions are made. The most recently published offering is the ‘Unruptured Intracranial Aneurysm Treatment Score (UIATS)’, generated via consensus sessions with world-renowned leaders.2 Notably, this iterative process is termed a ‘Delphi’ consensus, which should readily differentiate it from conventional scientific endeavors. The UIATS is a complex score that combines patient-related, aneurysm-related, and treatment-related factors, and attributes 0–5 points per item, resulting in two columns of numerical values—one favoring aneurysm repair and the other favoring conservative management. A divergence in score of ≥3 between the columns yields a ‘definitive’ management recommendation.2 With this system, several inconsistencies are concealed …
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 | 0.037 | 0.166 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.016 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 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".