The Importance of Linking Primary and Secondary Electronic Medical Records
Bibliographic record
Abstract
To the Editor: We would like to congratulate C. John Michet 3rd, et al 1 on their excellent paper highlighting the issues of relying on hospital data for epidemiological studies. Routinely collected medical data, such as those found in hospital case notes, are an increasingly important resource for research, and therefore understanding the strengths and weaknesses of these data is important to clinicians and … Address correspondence to Professor C.D. Mallen, Research Institute for Primary Care and Health Sciences, Keele University, Keele, Staffordshire ST55 BG, UK. E-mail: c.d.mallen{at}keele.ac.uk
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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 | 0.040 | 0.286 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.031 | 0.035 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".