Symptom Trajectories in Posttreatment Cancer Survivors
Bibliographic record
Abstract
BACKGROUND: Cancer survivorship following cancer treatment is uncertain as physical and psychological sequelae related to the disease or its treatment may persist. However, little is known about the experience of symptoms after treatment. OBJECTIVES: The purposes of this study were to (1) examine postchemotherapy (post-CTX) symptom trajectories in cancer survivors and (2) determine whether demographic characteristics predicted symptom trajectories. METHODS: One hundred patients who recently completed CTX for lung cancer, colorectal cancer, or lymphoma rated symptoms on an electronic patient care monitor system prior to ambulatory care visits. Latent growth curve analyses were conducted to examine the trajectories of pain, fatigue, sleep disturbance, distress, and depression for 16 months after initial CTX. RESULTS: Symptoms were present at the first follow-up visit following CTX (P < .0001) and persisted over 16 months. The depression trajectory was predicted by sex: males showed a convex curvilinear growth trajectory, whereas females showed a concave trajectory (P < .05). Higher distress was predicted by younger age (P < .05). CONCLUSIONS: Psychological and physical symptoms persisted over the 16-month period following CTX for the entire sample. Sex differences in coping could partially explain the different trajectories of growth for depression, but further studies are warranted. Younger patients may be more vulnerable for distress during this posttreatment phase. IMPLICATIONS FOR PRACTICE: The posttreatment surveillance plan for cancer survivors should include a comprehensive assessment of psychological and physical symptoms. Persistence of symptoms can be expected in some patients, and supportive interventions should be tailored according to symptom reports.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".