Recent developments in early detection strategies and \npopulation-based screening: the perspectives of cervical cancer \nand COVID-19
Bibliographic record
Abstract
Early detection strategies and population-based screening are important public \nhealth tools in early detection of disease and population surveillance. This work aimed to \nexamine cervical cancer and COVID-19 with a focus on new strategies for early detection \nand population-based screening data; these approaches can help detect pre-cancerous \nlesions before they develop into cervical cancer and can help to better understand the \nspread of the SARS-CoV-2 virus in the general population. While cervical cancer screening \nis a long-standing program, requiring review of existing programs and new \nmethodologies, the emergence of the COVID-19 requires an initial evaluation of new test \nmethodologies. Cervical cancer data was collected through testing enrolled patient \nspecimens and programmatic data, and COVID-19 data was collected from testing deidentified \npatient specimens. This dissertation is comprised of three studies (4 \nmanuscripts). The first study reviewed the Newfoundland and Labrador (NL) cervical \nscreening program to assess positivity and clinical disease endpoint and reviewed \nprogrammatic indicators to determine the ability of the program to detect pre-cancerous \nlesions. The second study evaluated the diagnostic indices and properties of CINtec PLUS \nand cobas HPV tests among those referred to colposcopy with a history of low-grade \nsquamous intraepithelial lesions (LSIL) to identify those at increased risk of pre-cancerous \nlesions and cervical cancer and potentially reduce the proportion requiring further followup \nin all age groups, for those <30 years of age, and those > 30 years of age. Finally, the \nthird study evaluated three (2 different IgG and 1 IgA) serological tests’ abilities to detect \nprior infection with SARS-CoV-2 from laboratory-confirmed COVID-19. \nThe findings indicate in the first study that while there have been attempts to \nimprove cervical screening participation, high rates of abnormalities, pre-cancerous \nlesions, and invasive cancers are troubling. In the second study, high sensitivity (93.2%) \nand negative predictive value (NPV, 98.1% for CINtec PLUS, 97.0% for cobas) were \nobserved in patients referred to colposcopy with a history of LSIL cytology for CINtec PLUS \ncytology and the cobas HPV test (CIN3+). However, the reduced sensitivity of CINtec PLUS \nfor detection of CIN2+ in general (81.8% for CINtec PLUS, 93.9% for cobas), and CIN 2, \nespecially in patients <30 years, needs to be considered in risk assessments if choosing \nLSIL-CINtec PLUS triage. Nevertheless, CINtec PLUS was consistently more specific than \nthe HPV test. In the third study, observed sensitivities ranged from 91.3-100.0% and \nspecificities of 90.8-98.2%; cross-reactivity was observed in the IgA test. A two-tiered \napproach was observed to improve performance in low prevalence settings. \nIn conclusion, based on the review of local cervical screening programs, there are \nopportunities for improvement. Either test examined could serve as a predictor of CIN3+ \nwith high sensitivity in patients referred to colposcopy with a history of LSIL regardless of \nage while significantly reducing the number of LSIL referral patients requiring further \ninvestigations and follow-up in colposcopy clinics. For COVID-19, IgG tests may serve as \npractical tools in helping detect past SARS-CoV-2 infection.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".