Medical decision making and the importance of baseline risk
Bibliographic record
Abstract
IntroductIonThere is general consensus that individual medical decisions should be made through a shared process between a patient and their clinician(s) with the goal of reaching a choice consistent with the patient's wishes. 1 The process asks clinicians to translate, in an understandable manner, the available knowledge that may affect a patient's decision. 2Our message is that the baseline risk (BR) for the outcome of primary interest is necessary information for any treatment decision, yet often ignored in discussions regarding medical decision making.For each potential treatment and outcome, the patient needs to understand the severity, the time frame, and a measurement scale to compare treatment choices.A variety of summary effect measures have been established to help patients compare treatment choices, for example, relative measures, such as relative risk (RR) and relative risk reduction (RRR), and absolute measures, such as absolute risk reduction (ARR), also known as risk difference (RD).Similar information given in different forms may result in different decisions. 3Relative measures can be difficult to interpret, 4 partly because patients need to know both the BR and the relative comparison, plus make the necessary multiplicative calculation to combine the two numbers.In 1988, Laupacis, Sackett, and Roberts recommended that clinicians translate risk contrasts to patients with a single, absolute number, that would incorporate 'both baseline risk without treatment and relative risk with treatment,' entitled, the 'number needed to treat' (NNT = 1/RD). 5he argument that NNT incorporates the BR is based on an assumption that the RR is constant across baseline risks for a particular disease and treatment.If RR were constant, then RD = BR*constant.Thus NNT appears directly correlated with the BR, implying that the BR provides no further important information for patient decision making. 6The purpose of this essay is to demonstrate that BR may affect the value that patients associate with any summary measure of effect.Consequently, BR must be provided to make an informed medical decision.
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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 | 0.079 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".