Could we clinicians be the greatest barrier to real progress in our field?
Bibliographic record
Abstract
In a recent editorial, Fiorella et al 1 marvel at the recent maturation of the neurointerventional surgical field, which they attribute to what they claim are ‘two seemingly diametrically opposed factors’: industry-driven research and evidence-based practice (emphasis ours).1 The thesis of the editorial is that, if randomized controlled trials (RCTs) are the gold standard, they are not feasible in many circumstances. Here is the list of situations for which RCTs are allegedly not ‘feasible’: ‘At the introduction of a new device’; ‘When devices are designed to treat diseases that have a poor natural history with standard management’; ‘When no suitable control group exists’; ‘When iterative technologies emerge to compete with existing technology’; ‘When the disease is insufficiently prevalent for an RCT to be completed’; ‘When there is no market to support an industry-sponsored trial’; and ‘When treatment is proven for the same disease in a different patient population’.1 Are there any indications left? When should our community properly test innovative treatments against the standard management that has existed up until then? If not at the introduction of the innovation, when uncertainty is maximal; if not later in the process, when clinicians are ‘accumulating critical case experience’, and not even when a second iteration emerges, then when? According to the authors, it is too late when ‘equipoise no longer exists’. Thus we are invited to consider innovative solutions. Unfortunately, the small case series with historical comparisons that the authors propose can hardly qualify as innovative: they are the very methods which have previously misled us and that …
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 | 0.039 | 0.226 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.033 | 0.048 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".