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Enregistrement W2601749821 · doi:10.1038/bjc.2017.87

Evaluating wait times from screening to breast cancer diagnosis among women undergoing organised assessment vs usual care

2017· article· en· W2601749821 sur OpenAlexafffundabout
Anna M. Chiarelli, Derek Muradali, Kristina M. Blackmore, Courtney R. Smith, Lucia Mirea, Vicky Majpruz, Frances P. O’Malley, May Lynn Quan, Claire Holloway

Notice bibliographique

RevueBritish Journal of Cancer · 2017
Typearticle
Langueen
DomaineMedicine
ThématiqueGlobal Cancer Incidence and Screening
Établissements canadiensHealth Sciences CentreFoothills Medical CentreSt. Michael's HospitalUniversity of TorontoSunnybrook Health Science CentreCancer Care Ontario
Organismes subventionnairesCanadian Institutes of Health ResearchCancer Care Ontario
Mots-clésMedicineBreast cancerLogistic regressionBiopsyRetrospective cohort studyCancerBreast biopsyAnxietyBreast cancer screeningMammographyGynecologyObstetricsInternal medicine

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Timely coordinated diagnostic assessment following an abnormal screening mammogram reduces patient anxiety and may optimise breast cancer prognosis. Since 1998, the Ontario Breast Screening Program (OBSP) has offered organised assessment through Breast Assessment Centres (BACs). For OBSP women seen at a BAC, an abnormal mammogram is followed by coordinated referrals through the use of navigators for further imaging, biopsy, and surgical consultation as indicated. For OBSP women seen through usual care (UC), further diagnostic imaging is arranged directly from the screening centre and/or through their physician; results must be communicated to the physician who is then responsible for arranging any necessary biopsy and/or surgical consultation. This study aims to evaluate factors associated with diagnostic wait times for women undergoing assessment through BAC and UC. METHODS: Of the 2 147 257 women aged 50-69 years screened in the OBSP between 1 January 2002 and 31 December 2009, 155 866 (7.3%) had an abnormal mammogram. A retrospective design identified two concurrent cohorts of women diagnosed with screen-detected breast cancer at a BAC (n=4217; 47%) and UC (n=4827; 53%). Multivariable logistic regression analyses examined associations between wait times and assessment and prognostic characteristics by pathway. A two-sided 5% significance level was used. RESULTS: Screened women with breast cancer were two times more likely to be diagnosed within 7 weeks when assessed through a BAC vs UC (OR=1.91, 95% CI=1.73-2.10). In addition, compared with UC, women assessed through a BAC were significantly more likely to have their first assessment procedure within 3 weeks of their abnormal mammogram (OR=1.25, 95% CI=1.12-1.39), ⩽3 assessment procedures (OR=1.54, 95% CI=1.41-1.69), ⩽2 assessment visits (OR=1.86, 95% CI=1.70-2.05), and ⩾2 procedures per visit (OR=1.41, 95% CI=1.28-1.55). Women diagnosed through a BAC were also more likely than those in UC to have imaging (OR=1.99, 95% CI=1.44-2.75) or a biopsy (OR=3.69, 95% CI=2.64-5.15) vs consultation only at their first assessment visit, and two times more likely to have a core or FNA biopsy than a surgical biopsy (OR=2.08, 95% CI=1.81-2.40). Having ⩽2 assessment visits was more likely to reduce time to diagnosis for women assessed through a BAC compared with UC (BAC OR=10.58, 95% CI=8.96-12.50; UC OR=4.47, 95% CI=3.94-5.07), as was having ⩽3 assessment procedures (BAC OR=4.97, 95% CI=4.26-5.79; UC OR=2.95, 95% CI=2.61-3.33). Income quintile affected wait times only in women diagnosed in UC, with those in the two highest quintiles more likely to receive a diagnosis in 7 weeks. CONCLUSIONS: Women with screen-detected breast cancer in OBSP were more likely to have shorter wait times if they were diagnosed through organised assessment. This might be as a result of women diagnosed through a BAC having more procedures per visit, procedures scheduled in shorter intervals, and imaging or biopsy on their first visit. Given the significant improvement in timeliness to diagnosis, women with abnormal mammograms should be managed through organised assessment.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,294
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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.

Tête enseignante Opus0,063
Tête enseignante GPT0,412
Écart entre enseignants0,350 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations34
Publié2017
Routes d'admission3
Résumé présentoui

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