Bibliographic record
Abstract
We have all heard people talk (or write) about the art and science of medicine. Sometimes we mean that there’s a certain art or craft to clinical medicine, the application of science to real people in real situations. Sometimes we are referring to the unpredictable human side of medicine, the relationships and rapport building essential to clinical and professional practice. Whether we refer to clinical judgment or communication skills, what we imply is that in science we “know” but in art we are “winging it.” Definitions of science include words such as facts, principles, laws, truth, knowledge, and systematic, while definitions of art include creative skill, imagination, appreciation, and beauty. In Roze des Ordons et al. (this issue, 2016) we read that a misplaced and inappropriate word might leave a more lasting and painful scar than a surgeon’s sloppy scalpel, or as they put it, “… unhelpful communication can cause iatrogenic suffering, with a lasting impact upon patients and families and residual uncertainty and emotional distress amongst trainees, [thus] difficult discussions should be considered as seriously as an invasive procedure.” Our interactions with patients can have lasting and profound consequences so we should not be “winging it.” We need to move the art and craft of communications to a higher level where principles and laws of human behaviour and complex interactions are systematically learned and skillfully applied.
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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.070 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 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".