Serum cytokine values and fatigue in chronic hepatitis C infection
Bibliographic record
Abstract
Patients with chronic hepatitis C infection often experience fatigue. In many clinical situations, an association between fatigue and altered serum cytokine levels has been found. Altered cytokine levels in patients with hepatitis C have not shown a correlation with the degree of serum transaminase elevation or pathological change on liver biopsy. The aim of our study was to examine whether there was an association between abnormal serum cytokine levels and fatigue in patients with compensated chronic hepatitis C. Patients referred to a tertiary care hepatology clinic who were hepatitis C antibody positive and who had elevated alanine aminotransferase (ALT) levels were eligible for entry into the study. A control group was also included. Subjects in both groups who had characteristics other than hepatitis C that were known to alter cytokine values and/or cause fatigue were excluded. Patients completed a validated questionnaire to determine their fatigue severity score (FSS). Bioassays were used to measure interleukin (IL)-1, IL-6 and tumour necrosis factor-alpha (TNF-alpha) levels in early morning serum samples taken from patients and controls. Altered cytokine values were defined as those more than two standard deviations above the mean control value. Data was analysed using SPSS, version 8.01. Of the 78 patients with chronic hepatitis C who participated in the study, 19 (24%), 24 (30%) and 45 (56%) had elevated levels of IL-1, IL-6 and TNF-alpha, respectively, compared with only two (6%) of the control group who had elevation of any of the three cytokines. No correlation was found between the FSS and serum cytokine levels, when analysed singly or in combination, in patients with chronic hepatitis C. Hence, alteration in early morning serum levels of IL-1, IL-6 and TNF-alpha in patients with chronic hepatitis C infection and elevated ALT levels bear no correlation with the symptom of fatigue.
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.
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) | 0.000 | 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) | 0.001 | 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".