Bibliographic record
Abstract
Current guidelines give evidence-based advice on how best to manage anaemia in patients with renal disease, but these guidelines do not consider individual patient needs, so tailoring anaemia management to each patient still remains a challenge for the treating physician. Two case studies are described that illustrate some of the key factors that need to be considered. The first case emphasizes that haemoglobin (Hb) targets recommended in current guidelines may not suit all patients. The patient had been stably maintained on subcutaneous epoetin therapy with an average Hb concentration of >13.0 g/dl because he developed angina symptoms when his Hb level fell to 12.2 g/dl. Iron deficiency was identified as the likely cause of falling Hb in this patient. After the patient's iron supplementation was increased, his Hb level was normalized back to >13.0 g/dl without increasing the epoetin dose, and the angina symptoms were resolved. The second case involved a pre-dialysis patient with diabetes, who required a higher dose of epoetin after beginning concomitant antihypertensive treatment with an angiotensin-converting enzyme inhibitor. Previously, the treatment of renal anaemia in pre-dialysis patients has not been the focus of attention. Two ongoing randomized controlled trials have been designed to study early initiation of epoetin treatment in pre-dialysis patients and will provide much needed information in this area.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.011 |
| 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".