The future of breast cancer research and practice in Asia, Latin America, and the Middle East/North Africa: a qualitative horizon scanning analysis.
Bibliographic record
Abstract
Abstract Abstract #5095 Objective: To conduct a horizon-scanning analysis to identify future needs, challenges and trends relating to breast cancer research and practices globally with special emphasis on emerging and understudied regions. Methods: Following the design and pilot of the survey instrument, data was derived from key informant interviews with 221 thought leaders in breast cancer in Asia (n=97), Latin America (n=46), Middle East/North Africa (n=39) and Australia and Canada (n=39). Thought leaders were identified using a combination of purposive and snowballing sampling and included oncologists, surgeons, other breast cancer specialists, advocacy leaders and policy makers. Transcripts and field notes were then coded and compared, and a taxonomy of 20 issues was developed. The propensity of these themes were then analyzed and compared across the four regions. Results: In Asia the most prevalent issues were i) building capacity for clinical research, ii) more nurses for patient care/education, iii) keeping up to date with new information and iv) increased targeted/personalized treatment. In Latin America key themes were the need to i) increase targeted/personalized treatment, ii) address disparities among the underserved, iii) high cost to third party payers, iv) increase capacity for clinical research and v) control out of pocket costs for patients. In the Middle East/North Africa key themes were the need for i) building capacity for clinical research, ii) more nurses for patient care/education, iii) increased public education on screening and iv) increased capacity for early detection. In Australia and Canada issues were i) keeping up to date with new information, ii) weighing the cost effectiveness of new treatments, iii) the need for increased data sharing and iv) improving communication between stakeholders. Discussion: While a number of respondents expressed observations relating to country specific etiology of disease (such as women presenting at younger ages and with more aggressive tumors) the lack of national registries and local clinical/genetic research make it hard to confirm these anecdotes scientifically. Finally, increased focus on the development of effective advocacy and policy leadership are needed in these regions to draw attention and resources towards issues of community empowerment, survivorship and quality of life which were neglected by all but a handful of respondents. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 5095.
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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 | 0.028 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".