The 2018 ter Brugge Lecture: Problems with the Introduction of Innovations in Neurovascular Care
Bibliographic record
Abstract
Most endovascular innovations have been introduced into clinical care by showing good outcomes in small enthusiastic case series of selected patients. Randomized clinical trials (RCTs) have rarely been performed, except for acute ischemic stroke, but even then most trial designs were too explanatory to inform clinical decisions. In this article, we review 2 × 2 tables and forest plots that summarize RCT results to examine methodological issues in the design and interpretation of clinical studies. Research results can apply in practice when RCTs are all-inclusive, pragmatic trials. Common problems include the following: (i) using restrictive eligibility criteria in explanatory trials, instead of including the diversity of patients in need of care, which hampers future generalizability of results; (ii) ignoring an entire line of the 2 × 2 table and excluding patients who do not meet the proposed criteria of a diagnostic test in its evaluation (perfusion studies) which renders clinical inferences misleading; (iii) ignoring an entire column of the 2 × 2 table and comparing different patients treated using the same treatment instead of different treatments in the same patients (the "wrong axis" comparisons of prognostic studies and clinical experience) which leads to unjustified treatment decisions and actions; or (iv) combining all aforementioned problems (case series and epidemiological studies). The most efficient and reliable way to improve patient outcomes, after as well as long before research results are available, is to change the way we practice: to use care trials to guide care in the presence of uncertainty.
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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 | 0.163 | 0.561 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.013 | 0.038 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".