Does Cell Size Matter?: Utilizing Mean Cell Volume in Hospitalized Patients As a Screen to Determine Common Causes of Anemia Including Iron Deficiency Anemia, Vitamin B12 and Folate Deficiency
Bibliographic record
Abstract
Abstract Background: The approach to anemia is traditionally based on the Mean Cell Volume. Based on this approach anemia is subdivided into microcytic, normocytic and macrocytic causes. This approach may not accurately discern common causes of anemia in hospitalized patients. Previous studies suggest the MCV may not be a sensitive measurement to differentiate iron deficiency anemia (IDA) and megaloblastic anemia due to vitamin B12 or folate deficiency. Methods: In a retrospective, single-centre study at London Health Sciences Center, all adult patients (age 18 years or older) with confirmed IDA, vitamin B12 and folate deficiency and their associated MCV and RDW values at LHSC over a one year period were reviewed. IDA was defined as hemoglobin less than 115 g/l and ferritin less than 30 (M) and 10 (F). Vitamin B12 deficiency was defined as a value of less than 145. Results: 1119 patients were identified with confirmed IDA, B12 or Folate deficiency. 894 patients had IDA of which 564 patients had low MCV (sensitivity 63.1%) and 797 patients had low MCV or high RDW (sensitivity 89.1%). Of the 96 patients with vitamin B12 deficiency anemia, 12 patients had high MCV (sensitivity 12.5%) and 70 patients had high MCV or high RDW (72.9%). Only one of 2244 patients who had RBC folate measured had an actual folate deficiency. Conclusion: Our results confirm that a normal MCV does not exclude IDA or vitamin B12 deficiency. Clinicians need to be aware of the low sensitivity of the MCV as a screen. The sensitivity of MCV for IDA or vitamin B12 deficiency is improved with indices such as RDW. Folate deficiency is rare in North America and should not be routinely ordered for assessment of nutritional anemia. Disclosures No relevant conflicts of interest to declare.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".