Patterns of follow-up in oncologic care across Canada.
Bibliographic record
Abstract
e20589 Background: With continual advancements in cancer care, improved outcomes and increasing survivor populations, cancer survivorship has become an important area of research. Methods: A 35-question electronic survey was sent to physician members of the Canadian Association of Radiation Oncologists. Based on their scope of practice, respondents were presented with brief clinical scenarios pertaining to various survivor populations. A subsequent series of questions were posed to determine routine follow-up practices. Results: In total, 111 radiation oncologists (RO) responded (44% response rate); 29% were female, 43% were in practice less than 10 years, and most regions of Canada were well represented. Most worked in centers staffed by more than 10 oncologists (69%), and saw more than 200 new patient consults per year (78%). 10% would not follow patients routinely, mainly in cases involving breast or prostate survivors. 73% of such patients (73%) would be followed by their primary care providers (PCP) whereas ROs would follow their CNS, GI, HN and GYNE patients. Lack of resources (55%) and a belief that follow-up by PCPs is equally effective (55%) were the top two reasons for not following patients. Treatment toxicity (92%) and the possibility of salvage or palliative treatment (86%) were the two most common reasons for routine follow-up. The majority (55%) of ROs follow patients for < 5 years, with 36% following for 5-10 years, and a minority (9%) following for longer than 10 years. 54% would not change the frequency of their follow-up, but 39% would decrease and only 7% would make no change. Workload and lack of resources were major barriers to follow-up, but in addition, many felt that follow-up by FPs or Advanced Practice Nurses could be equally effective. Some felt this would require additional training and more guidelines to make this effective. Conclusions: The majority of ROs follow their patients, especially when salvage treatment is possible. A significant portion would decrease their follow-up in frequency because of workload burden, resource limitations and a belief that there can or should be increased involvement from FPs and other allied health care providers.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".