Relationship between fatigue and cytokine levels in patients age 50+ with acute myeloid leukemia (AML)
Bibliographic record
Abstract
19566 Background: Fatigue is the most common and disabling symptom affecting patients with AML; effective prevention or treatment measures have yet to be found. Cytokines, biological markers of inflammation, may represent a major cause of fatigue, but published data are limited. Methods: Patients age 50 or older with AML were recruited between May and September 2006. All patients were fluent in English, within one year of diagnosis, and free of any other active malignancy. Fatigue was measured using the Functional Assessment of Cancer Therapy (FACT) Fatigue subscale, a single-item global fatigue scale, and the European Organization for the Research and Treatment of Cancer (EORTC) QLQ-C30. Blood was simultaneously drawn for quantitative measurement of a panel of 13 cytokines. Repeat measurements were done 4–6 weeks later. Correlational analysis was used to examine relationships between individual cytokines and fatigue scores. Changes in fatigue scores between time points were correlated with changes in cytokine levels. Results: 34 patients (23 men; 11 women) were enrolled (mean age 67 y; range 52–84). 27% had not started chemotherapy or were receiving best supportive care, while the rest were undergoing active chemotherapy. At baseline, a weak correlation (r=0.332, p=0.059) was seen with interleukin (IL)-6 and at least one fatigue measure. No correlations (r<0.30) were observed with any of the other cytokines (interferon (IFN)-?, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IP-10, MCP-1, MiG, and tumor necrosis factor-a) and any fatigue measure. Follow-up data were available for 29 patients. A statistically significant correlation with fatigue was seen with IL-2 (r=0.407, p=0.032), and clinically-important correlations that did not achieve conventional statistical significance were seen with IFN-? (r=0.331, p=0.085), IL-5 (r=0.344, p=0.073), and IL-10 (r=0.326, p=0.091). Conclusions: Based on these data, the most promising cytokine-fatigue relationship was noted with IL-2. However, IFN-?, IL-5, IL-6, and IL-10 also showed potentially important relationships with fatigue. Given our small sample size and patient enrolment at differing time points during their treatment course, further controlled studies are warranted. No significant financial relationships to disclose.
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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 | 0.000 | 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) | 0.002 | 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".