The relationship of haemoglobin level and survival: direct or indirect effects?
Bibliographic record
Abstract
The relationship between haemoglobin (Hb) level and survival in patients with chronic kidney disease (CKD) is complex. This paper explores the physiological basis for the hypothesis that Hb level and survival are causally related in this patient group, and assesses the current state of knowledge from clinical studies. Issues related to the methodology and analysis of clinical studies limit the certainty with which conclusions regarding the direct relationship between Hb level and survival can be drawn. The data support the concepts that Hb level is associated with survival in patients both with and without CKD, that changes in Hb level are associated with cardiovascular disease (CVD), and that CVD is prevalent in patients with CKD. Hb level is affected by nutritional status, inflammation, and the availability and effectiveness of human recombinant erythropoietin (rHuEPO) therapy, as well as by the degree of kidney function. Thus, the complexity of the relationships between Hb level, CVD and survival in patients with CKD requires further study from both the mechanistic and the clinical perspective. Properly designed clinical trials with survival as an endpoint, as well as data from prospectively measured modifiers of Hb levels and other markers of CVD, are needed to determine the physiological and statistical interaction of these factors in clinical practice.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".