Bibliographic record
Abstract
When Sir James Mackenzie was moving back to Scotland from London in 1918 to set up the Institute for Clinical Research in St Andrews he said that he wanted ‘To do for medicine what the Atomic Theory had done for chemistry’ .1 He was referring to the fact that Sir Ernest Rutherford had ‘split the atom’ the year before and ushered in a new scientific age through nuclear fission. Mackenzie saw an analogy with what he hoped to achieve in medicine through fusion of data across time and from various sources. He believed that the information recorded in GP consultations about the early stages of disease would lead to new ways to understand health and disease, leading to earlier and better diagnosis and treatment.2 He was frustrated then, as we often still are, by not knowing what the symptoms and signs our patients consult us about really mean. Mackenzie hoped that a group of family physicians who kept continuous records of all cases they saw, including details of the patients’ living conditions, dietary and smoking habits, and employment, would enable research to be carried out into the relationship between environmental factors and disease.3 In this idea, he was ahead of his time, but developments in using electronic medical records and practice-based research networks are now enabling his vision to be realised. This lecture will describe the extent to which the data recorded by GPs are already contributing to improved care for patients and discuss emergent opportunities in research internationally by describing how family physicians can now make significant contributions to medical science every day in their work.4 In a GP consultation such as that shown in Box 1 there are many data points that are already present in the patient’s record and many others that will …
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".