Concordance and timing in recording cancer events in primary care, hospital and mortality records for patients with and without psoriasis: a population-based cohort study
Bibliographic record
Abstract
Background: The association between psoriasis and the risk of cancer has been investigated in numerous studies utilising electronic health records (EHRs), with conflicting results in the extent of the association. Objectives: To assess concordance and timing of cancer recording between primary care, hospital and death registration data for people with and without psoriasis. Methods: Cohort studies delineated using primary care EHRs from the Clinical Practice Research Datalink (CPRD) GOLD and Aurum databases, with linkage to hospital episode statistics (HES), Office for National Statistics (ONS) mortality data and indices of multiple deprivation (IMD). People with psoriasis were matched to those without psoriasis by age, sex and general practice. Cancer recording between databases was investigated by proportion concordant, that being the presence of cancer record in both source and comparator datasets, and for risk factors of discordance. Delay between CPRD and HES records and predictors of discordance were assessed. Results: 58,904 people with psoriasis and 350,592 comparison patients were included using CPRD GOLD; whereas 213,400 people with psoriasis and 1,268,998 comparison patients were included in CPRD Aurum. For all cancer records (excluding keratinocyte), concordance between CPRD and HES was greater than 80%. Concordance for same-site cancer records was markedly lower (<68% GOLD-linked data; <72% Aurum-linked data). Concordance of non-Hodgkin lymphoma and liver cancer recording between CPRD and HES was lower for people with psoriasis compared to those without. Conclusions: Concordance between CPRD and HES is poor when restricted to cancers of the same site, with greater discordance in psoriasis patients for some cancers of specific sites. The use of linked data is an important step in reducing misclassification of cancer outcomes.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".