Routinely collected electronic health data and STI research: RECORD extension to the STROBE guidelines
Bibliographic record
Abstract
Electronic medical records (EMRs) are increasingly being used by health services including those that test for and treat sexually transmitted infections (STIs).1–3 The implementation of EMRs opens up new opportunities for improving the quality, effectiveness and efficiency of sexual health services and brings with it the potential for enhanced research capacity.1–3 Information from routinely collected health data can and should be leveraged for the evaluation of clinical services to improve STI and HIV care and to measure the impact of interventions aimed at curbing STI. This information has already been captured and, if appropriately harnessed, constitutes a rich repository of data that can be used for research. Because information in EMRs has generally been gathered for patient management rather than to answer research questions, the use of such data for studies introduces potential limitations and biases. When examining treatment outcomes retrospectively for instance, observational studies will naturally fall short of rigorously conducted randomised trials. However, prospective trials can be prohibitively expensive and their results may not be generalisable to diverse real world settings and populations. In addition, observational research carries with it specific …
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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 | 0.167 | 0.462 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.025 | 0.038 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".