Authors’ reply to Upton
Bibliographic record
Abstract
In response to Upton, the Clinical Practice Research Datalink (CPRD) includes limited anonymised information on the characteristics of health professionals.1 2 Sex and role are available and information on consultation volumes can be extracted. Data on GP density, full time equivalents, or other staff characteristics are unavailable. Such data are available nationally but cannot be linked to the practices in CPRD, which are anonymous. In other work applying our prescribing safety indicators to the Salford Integrated Record (SIR), we investigated the effects of whether or not a practice was a training practice and which electronic record system it used (Vision or EMIS).3 Neither of these factors was a significant explanatory variable in the logistic regression model. However, list size was the only measure of GP density accessible in this extract of the SIR. Regarding Upton’s comments about access to online test results in secondary care, we accept that this is a possibility—this is why we did not include the indicator related to prescribing warfarin without an international normalised ratio test in the composite monitoring indicator. Our earlier work using SIR containing linked primary and secondary care records informed this decision,3 which was reinforced by our observation that excluding the secondary care data from SIR had a large effect on this indicator (data available from authors). Excluding the secondary care data in SIR did not greatly affect the prevalence for the other monitoring indicators, suggesting that—in Salford at least—the information was also held within primary care records (data available from authors). As Upton suggests, clinical decision systems that are independent of the practice clinical computer system (such as EMIS and VISION) can be a complicating factor for certain indicators and might explain some of the variation across practices. Nevertheless, it can be argued that the availability of the relevant information in the patient’s clinical record is important, especially as continuity of care is often fragmented in large modern general practices.
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 | 0.012 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.044 | 0.074 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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".