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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".