Plasma Gelsolin and Its Association with Mortality and Hospitalization in Chronic Hemodialysis Patients
Bibliographic record
Abstract
BACKGROUND: Human plasma gelsolin (pGSN) is an actin-binding protein that is secreted into the extracellular fluid, with the skeletal muscle and myocardial tissues being its major source. Depletion of pGSN has been shown to be related to a variety of inflammatory and clinical conditions. METHODS: pGSN levels were prospectively determined in prevalent maintenance hemodialysis (HD) patients from 3 U.S. dialysis centers. Demographics (age, time since dialysis initiation, race, gender, body height and weight, comorbidities), inflammatory markers (C reactive protein, CRP; interleukin 6, IL-6), free triiodothyronine (fT3), and routine laboratory parameters were obtained. We performed Kaplan-Meier and Cox proportional hazard survival analysis for all-cause and cardiovascular mortality, and recurrent event survival analysis for hospitalization. RESULTS: We studied 153 patients; mean age was 60.5 ± 14.7; 52% were males. The mean pGSN level was 6,617 ± 1,789 mU/ml. In univariate analysis, pGSN was positively correlated with body mass index (r = 0.2, p = 0.01), pre-HD serum albumin (r = 0.247, p = 0.002), and pre-HD serum creatinine (r = 0.381, p < 0.001), and inversely with age (r = -0.286, p < 0.001), CRP (r = -0.311, p < 0.001), and IL-6 (r = -0.317, p < 0.001). In the adjusted analysis, the associations with CRP and creatinine were retained. pGSN levels tended to be lower in patients who died (p = 0.08). There was no association with all-cause or cardiovascular mortality, or all-cause hospitalization. Of note, fT3 was lower in patients who died (p = 0.001). CONCLUSIONS: Even though pGSN was inversely correlated with age, CRP and IL-6, suggesting that inflammation may influence pGSN, lower pGSN levels were not associated with hospitalization, all-cause and cardio-vascular mortality in this patient population.
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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 | 0.001 |
| 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) | 0.001 | 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".