Cancer survivorship: Creating a national agenda
Bibliographic record
Abstract
The cadre of individuals who are living after a diagnosis of cancer is growing steadily. In developed countries, as many as 78% of pediatric patients are alive five years following diagnosis, as are 60% of adult patients (Curtiss & Haylock, 2006). At present in Canada, about one million individuals are living as cancer survivors (Canadian Cancer Society, 2008). With the anticipated increase in the incidence of cancer around the world and the success of treatment approaches, it is anticipated this cadre will continue to grow. Unfortunately, cancer survivorship does not come without cost. It is becoming increasingly evident there are late and long-term effects cancer survivors experience, both physical and psychosocial, that can compromise quality of life and increase the burden of suffering. Compared to matched population controls, cancer survivors have significantly lower outcomes on many burden of illness measures and experience difficulties accessing appropriate health care for a broad range of chronic medical conditions (Yabroff, Lawrence, Clauser, Davis, & Brown, 2004; Earle & Neville, 2004). A history of cancer may shift attention from important health problems unrelated to cancer and the roles of primary care providers and specialists in cancer care of survivors are not yet clear. As well, quality of life issues are different for survivors than for individuals at the point of diagnosis and treatment. Cancer survivors face a range of physical and psychosocial challenges. Up to 75% of survivors have health deficits related to their treatments (Aziz & Rowland, 2003), more than 50% live with chronic pain (Lance Armstrong Foundation, 2004), 70% have experienced depression (Lance Armstrong Foundation, 2004), and between 18% and 43% have reported emotional distress (Vachon, 2006). Regardless of tumour type, there are commonly reported challenges: living with fear and uncertainty; changes in family roles; alterations in self image and self esteem; changes in comfort, physiological functioning and mobility; alterations in cognitive functioning; changes in employment and recreation; altered fertility and sexuality (Aziz & Rowland, 2003; Denmark-Wahnefried, Aziz, Rowland, & Pinto, 2005; Ganz, 2001; Lance Armstrong Foundation, 2005). Clearly, cancer survivors are a vulnerable population. New approaches are needed to overcome the barriers cancer survivors experience and ensure they receive appropriate care.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".