Bibliographic record
Abstract
It is very good news that people with cancer are living longer. The proportion of the population who are cancer survivors is on a steady increase with approximately 9.8 million cancer survivors in the US1 and an estimated 2.5% of the Canadian population.2 The most common prevalent cancers are breast, prostate and colorectal cancer. Taking all cancer types, two-thirds of individuals diagnosed today will survive at least beyond 5 years and be long-term survivors. If one considers breast and prostate cancer, over 80% will be long-term survivors.1 Approximately two-thirds of cancer survivors are over the age of 65. For a GP, as many as one in every six adults over the age of 65 years in their practice is likely to be a survivor of adult cancer.3 A survey of cancer survivors in the US has identified a range of physical, psychosocial and economic needs that are unmet, such as management of symptoms related to the primary treatment, depression, fear of recurrence, and problems related to employment and health insurance.4 The healthcare needs of the growing numbers of long-term cancer survivors is viewed as a challenge for cancer care specialists. It is a happy challenge that cancer care is no longer focused exclusively on treatment and palliation, and must now also consider how best to manage survivorship. In my view, however, this challenge rests squarely with GPs. An important aspect of the medical management of …
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".