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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".