Cancer genes - Management of carriers of breast cancer genes who have already developed cancer: the Carrier Clinic Model
Bibliographic record
Abstract
What is FACETContinuing professional education is now a well-established concept.For some professions providing evidence of continuing education and updating of skills has become or is becoming mandatory in order to retain the licence to practice.In health care, this differs from one profession to another and between countries.More and more there is the requirement for evidence based practice, i.e. practice must adapt of change as a result of the emergence of new evidence.In 1998, EJCC demonstrated its commitment to continuing professional education in oncology by introducing FACET.The March issue of each year focuses on an educational issue and the remaining three issues on research and development in cancer and palliative care.Each FACET comprises of four sections presented in an open learning context topic, triggers and challenges, literature and resources, and application to practice.As the reader we invite you to comment on the FACET content and we encourage you to submit for publication, if appropriate, any dialogue that our topics generate.Further information can be found on our Website: http://www.blackwell-science.com/ecc Topics under considerationThe topic selected may be a clinical topic, e.g.'fatigue', providing a statement of current thinking on cancer fatigue with some of the literature.A case example to illustrate or apply the science (depending on the topic as to whether a case is the best way to achieve this).Or a review of a piece of literature/challenge of the perceived wisdom stated above. Triggers and challengesThis is a set of activities for the reader to undertake.It will include triggers and challenges for all professions and then focus on specific professions to consider their views, experience and the roles in this topic area.No more than six activities will be presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".