Bibliographic record
Abstract
Shared decision making (SDM) consists of communication, collaboration, aspects of evidence based medicine, and relationship building-all based on the principle of the autonomy of the patient.It is a complex process with each consultation unique. 1 SDM, therefore, remains an ideal.This explains why it is uncertain whether any interventions for increasing the use of SDM by healthcare professionals are effective because the certainty of the evidence is low or very low. 2 National Institute for Health and Care Excellence recommendations are mostly based on moderate to very low randomised controlled trial evidence. 3 The Calgary-Cambridge consultation model used in undergraduate teaching has been honed by experts over many years.But it comprises 73 items-too long always to be helpful in a short interaction between doctor and patient. 4 The identification and eliciting of patients' ideas, concerns, and expectations (ICE) are key competencies for GPs in SDM.For more than 20 years the ICE acronym has helped me in my practice. 5 I like to compare my job as a GP with the work of the Flemish painter Jan van Eyck.In his time, Van Eyck had a revolutionary optics knowledge of the behaviour and properties of light, including its interactions with matter.In his portrait of Arnolfini 6 a convex mirror is placed like an eye in the centre of the composition, compressing and expanding the space at the same time.It is my job as a doctor to reveal, try to mirror, compress or expand the ideas, concerns, and expectations of my patients.This is a requisite for SDM.The literature on SDM is exploding and sharing decisions remains an art.Although I am not a gifted artist-painter with an incredible technique, I may practise the healing art of medicine.
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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 | 0.154 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.013 | 0.125 |
| Scholarly communication | 0.034 | 0.042 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.018 | 0.029 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".