Bibliographic record
Abstract
Andrew McGregor,* a 71-year-old man with a past history of hypertension and myocardial infarction, has recently been discharged from hospital after having an intracerebral haemorrhage (ICH).He wonders if he should restart aspirin, and asks for your advice.Patient-centred care and shared decision making underpinned by rigorous evidence-based medicine provide an ideal combination to answer areas of clinical equipoise such as the question asked by Mr McGregor.Research within primary care is able to help tailor treatment for patients, given the many daily consultations that GPs have with their patients, and whose multiple conditions require an integrated approach.1 Primary care research can also help to improve the application of health policy and its implementation, an increasingly bigger task that is likely to become more common in the future workload of all doctors. 2 Considering the time constraints that GPs have, with an increasing clinical and paper workload, financial pressures, and keeping up to date, it is understandable that many GPs may wonder what the purpose of research within primary care is and why should they participate?A report by Graham Watt defined its purpose:
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 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.075 | 0.021 |
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".