Development of a cervical cancer/screening database in Newfoundland and Labrador, Canada: A multi-linkage approach
Bibliographic record
Abstract
B2 Rationale: Cervical cancer is an almost entirely preventable cancer, yet statistics have consistently shown higher incidence and mortality rates due to cervical cancer in the province of Newfoundland and Labrador (NL) compared to Canada overall. NL has a large rural population and previous research indicates that rural women have suboptimal cervical screening rates, more advanced disease at diagnosis, and higher cervical cancer mortality rates. An initiative was untaken by the Provincial Cervical Screening Initiatives Program and the Newfoundland & Labrador Centre for Health Information (The Centre) to develop a comprehensive longitudinal administrative database for the study of cervical cancer and cervical cancer screening in NL. The present abstract describes the creation, characteristics, and potential of this province-wide database. Methods: Based on a unique provincial medical care plan number, administrative data were linked regarding information on cervical cancer, cytology screening, hospitalizations, mortality and fee-for-service physician claims. The database covers the period from 1995 to 2005. Cervical cancer and cytology data came from the Provincial Cancer Registry and Provincial Cervical Cytology Registry, provided by the Newfoundland & Labrador Cancer Treatment and Research Foundation. The Cancer Registry contains information on demographics, diagnosis, method of diagnosis, site, stage, morphology and behavior of cancer, patient status, as well as treatment and provider information. The Cytology Registry collects demographic information and cytology findings for Pap smears. Information on hospitalizations came from the Clinical Database Management System (CDMS) maintained by the Centre. The CDMS is the provincial discharge abstract database containing demographic, clinical and procedural data on all acute care and surgical day care hospitalizations in the Province. Mortality data came from the provincial Mortality Surveillance System also maintained by the Centre. The physician claims data came from the provincial Medical Care Plan which includes information on services provided, diagnosis and physician demographics. In the first step of the linkage, cytology data were merged with the cervical cancer cases. In the second and third steps, the cancer and cytology data were linked to the hospitalization and mortality data, respectively. In the fourth and final step in the linkage, the cancer and cytology data were linked to the physician claims records. The database was created in Microsoft Access format. Discussion: The comprehensive cervical cancer/screening database offers a unique opportunity for epidemiological research in the area of cervical cancer and cervical cancer screening. The approach used in this project is an innovative model for studying cervical cancer epidemiology, prevention and control, enabling researchers to investigate risk factors, health care services utilization, and other outcomes for cervical cancer and cervical screening at the population level.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".