Breast cancer optimal care timeframes for culturally and linguistically diverse populations and First Nations People: A regional centre experience in Australia.
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".