Breast cancer optimal care timeframes for culturally and linguistically diverse populations and First Nations People: A regional centre experience in Australia.
Notice bibliographique
Résumé
1587 Background: Culturally and linguistically diverse (CALD) populations and First Nations People are at-risk communities who face unique challenges in cancer diagnosis and management resulting in inequities. Optimal Care Pathways (OCP) established by Cancer Council Australia aim to address these disparities. The Breast cancer OCP outlines an integrated model of care with optimal timeframes such as time from general practitioner (GP) referral to specialist surgical review, time from decision to treat to surgery or neoadjuvant chemotherapy (NAC), and time from completion of NAC to surgery. Methods: Retrospective data was collected for all CALD (migrant from non-English speaking country and/or primary language identified as not English) and First Nations patients diagnosed with breast cancer treated at a regional centre in Australia (Townsville University Hospital) from 2018 – 2022. A comparison cohort (control) of consecutive non-CALD, non-First Nations patients was included. Data collected included patient demographics, tumour characteristics, stage, and identified timeframes which were compared with OCP. Results: 133 patients were included with 43 CALD (32%), 41 First Nations (31%) and 50 control (37%). CALD and First Nations cohorts had higher rates of stage IV disease at diagnosis (12 v 15%) compared to control cohort (0%). They were also more likely to be diagnosed via emergency department admission (CALD 16 v First Nations 7%) compared to control cohort (0%) suggesting later presentation. Of those referred through OCP defined GP pathway, a similar percentage were reviewed by specialist surgeon within optimal 2-week timeframe in all groups (CALD 47%; First Nations 39%; control 44%). Median time from decision to treat to surgery were longer in CALD versus control groups (19 v 13 days; p = 0.03), and in First Nations versus control groups (22 v 13 days; p = 0.02). Less CALD (89%, n = 24) and First Nations (82%, n = 18) patients underwent surgery within optimal 5-week timeframe compared to control (98%, n = 40). Similarly, median time from decision to treat to NAC were longer in CALD versus control groups (19 v 14 days; p = 0.05), and First Nations versus control groups (20 v 14 days; p = 0.03). Most patients (91%, n = 29) commenced NAC within optimal 4-week timeframe; 2 CALD and 1 First Nations patients did not. Median time from completion of NAC to surgery was longer in CALD versus control groups (29 v 24 days; p = 0.15), and in First Nations versus control groups (35 v 24 days; p = 0.04). Of those who recieved NAC, 100% CALD (n = 9), 69% First Nations (n = 9), and 89% control (n = 8) patients underwent surgery within optimal 4-week timeframe. Conclusions: Achievement of key OCP timeframes was lower in both CALD populations and First Nations People. Strategies need to be further developed to address the delays and health outcome disparities in these vulnerable cohorts.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
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 ».