Symptom assessment of oldest old cancer patients.
Bibliographic record
Abstract
e22035 Background: The older population is a growing segment of the United States. The oldest old, described as those aged 85 and older, experienced the fastest growth. Although this population is also undergoing active cancer treatment, there are limited studies evaluating the symptoms of the oldest old cancer patient population. Our study aimed to evaluate symptom severity as assessed by the Edmonton Symptom Assessment Scale (ESAS) of the oldest old (OO), older adult (OA) and general adult (OA) outpatient cancer patients on initial consult and followup visit. Methods: Retrospective review of 441 patients composed of 200 patients in the 18-64 age group (GA), 200 in the 65-84 age group (OA) and 41 in those over 85 years old (OO). Demographic and clinical characteristics were collected and the differences between the groups at initial consult and first follow-up using Chi-Squared, Kruskal-Wallis and Wilcoxon signed-rank tests. Results: The OO reported mild to moderate symptom severities on initial consult in all categories except nausea and spiritual pain, where median score was zero. Symptom severity of the OO was better than the younger population in pain and nausea, and similar in fatigue, depression, anxiety, and sleep. The OO reported highest severities in drowsiness and feeling of wellbeing. The OA reported highest severities in appetite loss and dyspnea. Dyspnea (p = 0.0225), financial distress (p = < 0.0001), and spiritual pain (p = 0.0026) were significantly associated with age on initial consult. For the OO, symptoms mostly improved on followup except for fatigue and spiritual pain. Anxiety (p = 0.0284) and sleep (p = 0.0486) significantly improved on follow-up visit. Conclusions: Our study shows that the oldest old cancer patients had significant symptoms on initial consult, with several physical symptoms such as fatigue, appetite, drowsiness, and feeling of wellbeing in the range of moderate severity. In the first palliative care followup visit, many symptoms decreased in intensity. With continued palliative care support, these changes may result in clinically important symptom improvements. More research is needed to address the needs of this growing cancer population and focus on symptoms that can improve with palliative care intervention.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".