Bibliographic record
Abstract
We have previously written about ‘denominator fallacy’ and its importance in the way that we report and interpret results, especially for endovascular treatment of acute stroke.1 In most studies, the number of patients going for endovascular thrombectomy (EVT) is taken as the denominator and the number of these patients achieving a modified Rankin Scale (mRS) of 0–2 as the numerator. The number of patients taken for EVT is dependent on the overall set-up, the view of the interventionalists, economic considerations (in some jurisdictions), imaging criteria, and clinical criteria. Of these, imaging criteria probably play a key role: the more stringent the imaging criteria (taking only patients with a very small core, etc), the smaller the number of patients who will go for EVT and the higher the likelihood of good clinical outcome (as a percentage of patients undergoing EVT). However, the more stringent the criteria, the smaller the overall impact of the treatment on the population as a whole. I used examples to illustrate this concept in a previous editorial. However, let us take this line of reasoning a step further. We know that time is brain and that infarcts grow during the hyperacute phase. At time zero after onset of symptoms, the size of the infarct core is zero. At 24 hours after onset, most infarcts are fully grown. …
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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 | 0.175 | 0.462 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.019 | 0.046 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".