Prediction of anemia on unenhanced computed tomography of the thorax.
Bibliographic record
Abstract
OBJECTIVE: To determine if anemia can be predicted on unenhanced computed tomography (CT) of the thorax. METHODS: Hemoglobin and hematocrit levels were obtained within 24 hours of the unenhanced CT scan of the thorax of 200 patients. Anemia was defined as a hemoglobin level less than 140 g/L for men and less than 120 g/L for women. Regions of interest were placed on the left ventricular cavity, aorta and the interventricular septum if visualized. The attenuation of the interventricular septum and left ventricular cavity were correlated with the presence or absence of anemia. RESULTS: When the interventricular septum was not visualized, for every 1 Hounsfield unit (HU) increase in left ventricular attenuation, hemoglobin increased by 0.435 g/L (SE = 0.253, p < 0.001). Failure to visualize the interventricular septum did not exclude the presence of anemia in either sex. When the interventricular septum was visualized, 100% of males and 89% of females met the criteria for the diagnosis of anemia. The prediction of anemia by visualization of the interventricular septum alone yielded a sensitivity of 75.4% and a specificity of 90.3%, with 80% of patients correctly predicted. The multiple regression analysis model yielded a sensitivity of 94.2% and a specificity of 67.7%, with 86% of patients correctly predicted. CONCLUSION: The diagnosis of anemia should be suggested whenever the interventricular septum is visualized on unenhanced CT.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".