Extracting primary care records for prostate cancer patients in the CHHiP multicentre randomised control trial: A healthcare data linkage study
Bibliographic record
Abstract
IntroductionThe aim is to investigate the effect of cardiovascular and diabetes comorbidities on radiotherapy-related side-effects in prostate cancer. Previous research suggests that comorbidities increase the risk of side-effects, but some cardiovascular medications may reduce symptoms by protecting against radiation damage. The evidence is inconclusive and mechanisms are not fully understood. ObjectiveTo explore whether routine primary care data can supplement clinical trial data in evaluating the impact of comorbidities and prescription medications on patient outcomes. ApproachThe CHHiP radiotherapy trial (CRUK/06/16) recruited 3,216 prostate cancer patients from 71 centres in UK, Ireland, Switzerland, and New Zealand between 2002 and 2011. Baseline comorbidity and radiotherapy-related side-effects over time were recorded. This was linked to computerised medical records (CMRs) from the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) database. RCGP RSC is a network of 192 English general practices with over 2 million patients (2.8% of the population). ResultsThe English population of CHHiP patients (N=2811) was used. 120 CMRs were linked, which exceeded the estimation of 79 linked records. However, six CMRs showed no evidence of regular GP care and a further eight patients were not recruited into the CHHiP trial until after they had de-registered from an RCGP RSC practice. Information on cardiovascular and diabetes comorbidities was extracted for 106 patients. The mean age was 69±7 years, representative of the CHHiP population. From the CMRs, 23 (22%) patients had diabetes and 47 (44%) had hypertension including 37 (35%) who took angiotensin converting enzyme (ACE) inhibitors (medications lowering blood pressure). In addition, 44 (41%) patients took aspirin, 65 (61%) statins (lowering blood lipids) and 14 (13%) took metformin (lowering blood sugar levels). Conclusion/ImplicationsThe small sample limits statistical analysis. However, a clinical trial was successfully linked to GP data to determine comorbidities and medications of patients. This will serve as a pilot for further research. The advantage of data linkage is that it may provide a mechanism for long-term follow-up of radiotherapy-related side-effects.
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 | 0.050 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".