Celebrities and screening: a measurable impact on high-grade cervical neoplasia diagnosis from the ‘Jade Goody effect’ in the UK
Bibliographic record
Abstract
BACKGROUND: The celebrity Jade Goody's cervical cancer diagnosis was associated with increased UK cervical screening attendance. We wanted to establish if there was an increase in high-grade (HG) cervical neoplasia diagnoses, and if so, what the characteristics of the women with HG disease were. METHODS: We analysed prospective data on 3233 consecutive colposcopy referrals in North East London, UK, from 01 April 2005 to 30 June 2010. Characteristics and outcomes of pre- and post-Goody cohorts were compared. RESULTS: Goody's diagnosis was associated with an increased incidence of colposcopy referrals in all subsequent annual quarters (incidence rate ratio (IRR) 1.3-1.9, P<0.002-P<0.0005) and increased HG disease diagnoses in the fourth quarter 2008/2009 (IRR 1.3, P=0.05) and first quarter 2009/2010 (IRR 1.3, P=0.07). We observed 1.90-fold (CI: 1.06-3.39), 2.06 (CI: 1.13-3.76) and 2.13-fold (CI: 1.07-4.25) respective increases in the odds of HG disease women being screening-naive in the first and second quarter 2009/2010, and the first quarter 2010/2011 (P<0.04, P<0.02 and P<0.04, respectively). There was a 2.23-fold increase in the odds of screening-naive HG disease women being symptomatic post-Goody's diagnosis (P=0.023). The age distributions of the pre- and post-Goody cohorts did not differ in any study group. CONCLUSION: Continued publicity about celebrities' diagnoses might encourage screening in at-risk populations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".