Radiotherapy Side Effects: Integrating a Survivorship Clinical Lens to Better Serve Patients
Bibliographic record
Abstract
The Canadian Cancer Society estimated that 220,400 new cases of cancer would be diagnosed in 2019. Of the affected patients, more than 60% will survive for 5 years or longer after their cancer diagnosis. Furthermore, nearly 40% will receive at least 1 course of radiotherapy (rt). Radiotherapy is used with both curative and palliative intent: to treat early-stage or locally advanced tumours (curative) and for symptom management in advanced disease (palliative). It can be delivered systemically (external-beam rt) or internally (brachytherapy). Although technique improvements have drastically reduced the occurrence of rt-related toxicity, most patients still experience burdensome rt side effects (seffs). Radiotherapy seffs are local or locoregional, and manifest in tissues or organs that were irradiated. Side effects manifesting within weeks after rt completion are termed "early seffs," and those occurring months or years after treatment are termed "late seffs." In addition to radiation oncologists, general practitioners in oncology and primary care providers are involved in survivorship care and management of rt seffs. Here, we present an overview of common seffs and their respective management: anxiety, depression, fatigue, and effects related to the head-and-neck, thoracic, and pelvic treatment sites.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".