Interventional neuroradiology: the role of experimental models in scientific progress.
Bibliographic record
Abstract
SUMMARY: The ultimate methodology necessary to adopt a treatment as generally beneficial is the randomized controlled trial, a method designed by and for clinicians to maximize the care of their patients in the presence of uncertainty. Some selection is however necessary to limit trials to more promising and less risky endeavors. Experimental models are the privileged answer to the problem of finding scientific evidence while refraining from harming patients in the course of this pursuit. They allow a step by step assessment, from simple but artificial settings to more complex and realistic animal models. But the use of animal models can only be justified if the community can be convinced that alternatives have been considered but are invalid, when the project is scientifically sound and methodologically irreproachable. As neurointerventional methods develop and gain wider clinical applications, progress should proceed in an orderly fashion, within limits set by prudence and human values, from the less risky, costly, time consuming methods, to the more definite, pragmatic, labor intensive but inescapable clinical trials. Each step is essential and the sequence cannot be violated without risks of errors that eventually translate into clinical morbidity.
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 | 0.203 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.024 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| 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".