Bibliographic record
Abstract
Risk tells us that uncertainty exists about a specific outcome.1 Particularly when great uncertainty exists about the outcome of a procedure, the communication of risk is challenging and difficult. Medical jargon and statistics have been employed to project scientific objectivity to help manage the emotional load that often accompanies discussions in which great uncertainty exists. These approaches may create distance between physicians and patients and raise questions about informed consent. Interestingly, little attention has been focused on this physician–patient interaction, leaving best practice about the communication of high risk unknown.2 How then are we to proceed to discuss risk with our patients? In the 1 August issue of Heart , Schaufel et al 3 bravely explored the nature of patient–physician dialogues about high-risk cardiac procedures. The authors invited us to consider that communication regarding high-risk procedures is about more than the procedure and the likelihood of complications and death; it is about unspoken aspects of the human condition for both the patient and the physician. It is particularly striking that these critical conversations were of relatively short duration—6–16 minutes. This is somewhat staggering if one considers the complexity of the legal, ethical and existential aspects involved in the course of communicating risk and obtaining informed consent. Communicating risk places great demands on both the physician and the patient. Complicated information must be translated by the physician using words or numbers that the individual before him can comprehend; the information must be imparted in a way that the physician …
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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 | 0.012 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".