Putting the future in service of the present: Risk assessment in acute coronary syndrome patients
Bibliographic record
Abstract
Risk, the possibility of loss or injury,\nis indeed a fixture in all aspects of\nour lives, from investing in the stock\nmarket to crossing the street. This\nconcept that we now take for\ngranted is in fact relatively novel.\nSome have argued that the ability to\ndescribe, estimate and control risk is\na key distinction between past and\nmodern times.1 In early civilization,\nthe future of human beings was\nlargely thought to be at the whim of\nthe gods. The turning point came\nduring the Renaissance when\nChevalier de Méré, a French\nnobleman with an affinity for\ngambling and mathematics,\nchallenged the famed French\nmathematician Blaise Pascal to\nsolve an infamous puzzle: How to\ndivide the stakes of an unfinished\ngame of chance between two\nplayers when one of them is\nahead.1,2 Collaboration between\nPascal and Pierre de Fermat, a\nlawyer and a talented\nmathematician, resulted in a solution\nand consequently, the theory of\nprobability was born. And it is this\nconcept that is at the heart of\nmodern cardiovascular medicine and\nresearch.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".