Histological Diagnosis of Sickle Cell Trait
Bibliographic record
Abstract
Hemoglobin electrophoresis is the method most commonly used to diagnose sickle cell trait (SCT) at autopsy. However, in some cases, this accepted technique is unable to be used due to either insufficient sampling, sample degradation, or lack of forethought; histology samples and/or gross tissue are not subject to these sampling errors and are routinely taken during autopsies. In this study, we attempted to determine whether one can reliably diagnose SCT using histology only. Histology sections of commonly sampled tissues (primarily heart, lung, and liver) from 9 decedents with SCT, 3 decedents with hemoglobin SC disease, and 18 control cases were examined in a blinded fashion as single slides and then as slide sets. When evaluating slide sets, the reviewers were able to identify the cases with SCT (sensitivity = 95%, specificity = 100%). Such samples could be used to diagnose SCT even decades after the original death certification and long after samples necessary for other techniques have degraded or been discarded.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".