Bibliographic record
Abstract
Concerns about survivorship and the needs of cancer survivors are occurring with increasing frequency (Ferral, Virani, Smith & Juarez, 2003; Curtiss & Haylock, 2006). Advances in the diagnosis and treatment of cancer have resulted in an ever-growing cadre of individuals who are survivors of the disease. In United States alone, there are more than 10 million cancer survivors (ACS, 2005). In Canada, there are almost 800,000-a comparable number given the country's population (NCIC, 2006). Approximately 60% of adults who are diagnosed with the disease and 78% of the children are alive at five years (ACS, 2005). Given the expectation that the number of people diagnosed with cancer will double in the next 40 years, we can expect the number of survivors will also continue to increase. Living after a diagnosis of cancer and its subsequent treatment is not without its challenges. We are only now beginning to recognize some of the concerns and issues survivors face and what a vulnerable population these individuals constitute. The growing number of individuals in our midst has allowed us to start learning about the challenges survivors can face on a daily basis. Their voices are being heard as advocacy group representatives speak out about their needs and the gaps in cancer service delivery. We are beginning to identify the spectrum of late complications survivors may experience with the potential to compromise quality of life. We are also beginning to recognize that the late and long-term effects are more prevalent, serious, and persistent than was originally expected.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".