Lack of consensus in calculation of interval cancer rates for cervical cancer screening
Bibliographic record
Abstract
INTRODUCTION: In 2018, nondisclosure of results of retrospective audits of cytology in interval cancers precipitated a crisis in the Irish national cervical screening programme. In response, an Expert Reference Group was convened which recommended a collaborative approach to the development of a new key performance indicator, the interval cancer rate. The Expert Reference Group also recommended that the Irish programme should collaborate with international colleagues to reach consensus on (i) the definition of an interval cervical cancer, (ii) the methodology to calculate the interval cancer rate, and (iii) benchmarking with other international programs. This study was undertaken to determine if a consensus regarding the definition of an interval cervical cancer and the calculation of an interval cancer rate exists. MATERIAL AND METHODS: A web-based questionnaire was sent to 18 population-based cervical screening programs. Inclusion criteria involved (1) a national or regional population-based cervical screening prograe; (2) a country or region with a population ≥population of Ireland; (3) programs located in Europe, Australia, or Canada; (4) programs that had responded to a previously published international survey on the disclosure of retrospective cytology reviews in cervical cancer cases. RESULTS: The response rate was nine out of 18. Of nine respondents, six had an agreed definition of interval cervical cancer, and four of these calculated an interval cancer rate. Three programs neither calculated interval cancer rates nor had any guidelines related to this. Of the six with an agreed definition, all respondents defined the numerator as invasive cancers in the screening age group, with four including microinvasive disease. Respondents included cancers diagnosed 3-5 years after the last screening test had been taken. Three respondents also included cancers diagnosed in women up to 3.5 years after they exited the screening program. Countries use different denominators, including (i) per women years, (ii) per number of screens, and (iii) per total cancers in screened population. CONCLUSIONS: There is variation in the parameters used in interval cancer rate calculation. To allow benchmarking of cervical screening program performance, there is a need for consensus on a standardized method of interval cancer definition and interval cancer rate calculation.
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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 | 0.380 | 0.564 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".