Ten Commandments for patient-centred treatment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 …
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it