Treating cancer patients. Practical monitoring and management of therapy-related complications.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To review investigation and management of some common long-term complications associated with cancer chemotherapy and radiation therapy. QUALITY OF EVIDENCE: Databases searched using MeSH key words "cancer chemotherapy," "cancer chemotherapy complications," "radiation therapy," and "radiation therapy complications" included Ovid and CANCERLIT. Overall the literature in this area is not strong; treatment guidelines and consensus conferences generally are lacking. Recommendations in this paper are mainly based on the results of individual studies and case reports, as few randomized controlled trials have been performed. Where appropriate, recommendations incorporate results of published treatment guidelines and consensus conferences. MAIN MESSAGE: For most solid tumours, patients should be most frequently monitored during the first 3 years after completing initial treatment for cure. Follow-up monitoring usually incorporates physical examination as well as radiologic and laboratory investigations. Patients should not be lost to follow up once treatment is completed, but monitored regularly, especially while they are at highest risk for disease recurrence. Long-term complications associated with cancer therapy include postsplenectomy sepsis syndrome; central and peripheral nervous system toxicities; ocular complications; thyroid, pituitary, testicular, or ovarian dysfunction; pulmonary toxicity; vascular or lymphatic, gastrointestinal, or osseous complications; genitourinary problems; and possible secondary malignancy. CONCLUSION: Primary care physicians are key to facilitating appropriate follow up of treated cancer patients. To do this, they must be aware of practical aspects of monitoring and management of therapy-related complications.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it