Bibliographic record
Abstract
Despite the use of recombinant human erythropoietin (rh-EPO, epoetin) for more than a decade in treating renal anaemia, there is still considerable debate over optimal target haemoglobin (Hb) levels. Current European and North American guidelines that are based on decade-old trials aim for partial anaemia correction, with a subnormal target Hb concentration. More recent randomized clinical trials examining the effect of normalizing Hb levels have produced conflicting results. A study in the USA, in patients with existing congestive heart failure or ischaemic heart disease, showed an unexpected rise in cardiac mortality and haemodialysis access failure with higher Hb levels. In contrast, three other studies (in Australia, Spain and Canada) that normalized Hb levels in healthier dialysis patients observed improvements in quality of life and exercise capacity and a slower progression of left ventricular dilatation, without an unacceptable increase in the incidence of adverse effects. These studies indicate that, while higher Hb levels may be detrimental to patients with pre-existing cardiac disease, healthier patients benefit from normalized Hb levels. Thus, there is no clear scientific rationale for setting a single Hb target for all patients, and individualized treatment targets would appear to be a more logical and patient-centred approach.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".