Ten Commandments for patient-centred treatment
Bibliographic record
Abstract
When deciding on a treatment, the first diagnosis you need to reach is about the nature of the illness. The second diagnosis you need concerns what the individual would like to achieve.1 Both are of equal importance and this is as true in simple one-off encounters as in complex lifelong illness. But the balance needs particularly careful thought when beginning long-term treatment. Always make sure that you understand your patient’s aims before you propose a course of action. It may require 3 minutes in a situation like an acute sore throat, or years of ongoing dialogue in a situation like multiple sclerosis or heart failure. Do not assume that you know what your patient has come for, and do not assume that the treatments you have on offer meet the goals of everyone in the same way. Both health professionals and lay people tend to overestimate the benefits of treatments and underestimate their harms. The traditional way to express these is as the number-needed-to-treat (NNT) and the number-needed-to-harm (NNH). It is important to have a ‘ball-park’ idea of these figures in common clinical situations, but also important to bear in mind their limitations. First, patients mostly find NNTs and NNHs hard to understand.2 Second, the numbers do not apply to individuals equally but are just average figures across the populations of clinical trials. Third, people vary widely in how they would balance a given benefit against a given harm.3 So we need better ways of a) knowing the true NNT and the NNH in the populations we treat; b) sharing this knowledge with people in ways they can understand; and c) applying this knowledge to the goals and preferences of the individual in front of us. The first commandment assumes that there will be two diagnoses in …
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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 | 0.071 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.017 | 0.041 |
| Insufficient payload (model declined to judge) | 0.075 | 0.019 |
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".