Bibliographic record
Abstract
Anemia in Patients With Advanced Heart FailureWe applaud Nanas et al. (1) on their attempt to elucidate the etiology of anemia in patients with advanced heart failure.We believe that clarification of some issues in their study regarding the definitions of anemia as well as potential introduction of surveillance bias would further improve its relevance.Defining anemia as "clinically significant" deviates from other standardized definitions-the World Health Organization (2) or Centers for Disease Control and Prevention criteria (3)-with implications for anemia prevalence.In addition, surveillance bias seems likely as only 2 of 37 patients were female derived from an unknown denominator.The ferritin concentration used by the investigators (Ͻ17 ng/ml, 38 pmol/l) is lower than other surveys (4) and expert reviews (5), hence, had a different threshold been applied, additional patients with iron deficiency anemia may have been discovered.Finally, the meaning of "the absence of iron stores" in the bone marrow is unclear without quantification or reporting of the stain used or inter-reader reliability.Additional data on the saturation index and correlation of serum markers with histological bone marrow findings would be most useful to other researchers in this area and potentially enhance the clinical applicability of the data.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".