Bibliographic record
Abstract
Abstract Anaemia, a decrease in erythrocyte mass that is reflected in a reduction in the haemoglobin concentration in the peripheral blood, is the most common haematologic condition for which medical attention is sought. Erythrocytes are critical to tissue oxygenation and carbon dioxide removal, and severe anaemia may lead to tissue hypoxia and organ dysfunction. Erythropoiesis is a highly regulated process controlled by erythropoietin. Adaptive mechanisms permit moderate anaemia to be well tolerated, and the clinical importance of mild to moderate anaemia is its representation of an underlying disease. Mild to moderate anaemia may be one of the first clues of an underlying disease. Although many of the signs and symptoms of anaemia are nonspecific, a pragmatic classification of anaemia based on integration of kinetic and morphologic characteristics of erythrocytes allows efficient investigation of the underlying aetiology. Key Concepts Anaemia is the most common haematologic condition encountered in medical practice Erythrocytes are critical to tissue oxygenation and carbon dioxide removal Physiologic compensatory mechanisms allow mild to moderate anaemia to be well tolerated; severe anaemia may be associated with tissue hypoxia and organ dysfunction Erythropoiesis, the production of erythrocytes, is a tightly regulated process controlled predominantly by the synthesis of erythropoietin in response to hypoxia The clinical significance of mild–moderate anaemia lies in its association with an underlying systemic disease; the finding of anaemia should prompt consideration of additional investigations Classification of anaemia based on kinetic and morphologic erythrocyte characteristics allows pragmatic segregation into microcytic, macrocytic or normocytic anaemia, further directing the investigation of an underlying disease
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".