Bibliographic record
Abstract
The foundation of laboratory hematologic diagnosis is the complete blood count and review of the peripheral smear. In patients with anemia, the peripheral smear permits interpretation of diagnostically significant red blood cell (RBC) findings. These include assessment of RBC shape, size, color, inclusions, and arrangement. Abnormalities of RBC shape and other RBC features can provide key information in establishing a differential diagnosis. In patients with microcytic anemia, RBC morphology can increase or decrease the diagnostic likelihood of thalassemia. In normocytic anemias, morphology can assist in differentiating among blood loss, marrow failure, and hemolysis-and in hemolysis, RBC findings can suggest specific etiologies. In macrocytic anemias, RBC morphology can help guide the diagnostic considerations to either megaloblastic or nonmegaloblastic causes. Like all laboratory tests, RBC morphologies must be interpreted with caution, particularly in infants and children. When used properly, RBC morphology can be a key tool for laboratory hematology professionals to recommend appropriate clinical and laboratory follow-up and to select the best tests for definitive diagnosis.
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 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".