Users' Guides to the Medical Literature
Bibliographic record
Abstract
Clinicians can use research results to determine optimal care for an individual patient by using a patient's baseline risk estimate, clinical prediction guidelines that quantitate an individual patient's potential for benefit, and published articles. We propose that when clinicians are determining the likelihood that treatment will prevent the target event (at the expense of adverse events) in a patient that they also incorporate the patient's values. The 3 main elements to joint clinical decision making are disclosure of information about the risks and benefits of therapeutic alternatives, exploration of the patient's values about both the therapy and potential outcomes, and the actual decision. In addressing the patient's risk of adverse events without treatment and risk of harm with therapy, clinicians must recognize that patients are rarely identical to the average study patient. Differences between study participants and patients in real-world practice tend to be quantitative (differences in degree of risk of the outcome or responsiveness to therapy) rather than qualitative (no risk or adverse response to therapy). The number needed to treat and number needed to harm can be used to generate patient-specific estimates relative to the risk of the outcome event. Clinicians must consider a patient's risk of adverse events from any intervention and incorporate the patient's values in clinical decision making by using information about the risks and benefits of therapeutic alternatives. JAMA. 2000;283:2829-2836
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.040 | 0.036 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.556 | 0.480 |
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".