Recent developments in early detection strategies and \npopulation-based screening: the perspectives of cervical cancer \nand COVID-19
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,005 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».