Acute Chest Syndrome: Can a Chest Radiograph Predict the Course Severity of the Disease?
Bibliographic record
Abstract
Background : The severity and predictability of acute chest syndrome (ACS) varies among patients with sickle cell disease. In our study we analyze whether the pathology identified in the first chest radiograph predict the course severity of ACS. Methods : We retrospectively reviewed the clinical records and radiographs of 79 episodes of acute chest syndrome in 63 patients with sickle cell disease. We established three categories of severity based on the following parameters: length of admission more than three days, presence of hypoxia, intensive care unit stay, and need for intubation. Two radiologists independently reviewed the first chest radiograph performed on the day of admission. The radiologist graded the degree of pathology and assigned it to one of four levels. Level 1 was defined as complete whiteout of the lungs or consolidation in any lobe with existence of pleural effusion; Level 2 consolidation in 4 lobes; Level consolidation in 3 lobes and level 4 as consolidation in 2 lobes. Results : We calculated the sensitivity, specificity and receiver-operating curve in all three severity categories. In all categories the area under the curve of the receiver-operating curve was above 0.5, archiving statistical significance. Conclusion : Patients presenting with multiple lobe involvement in the first chest radiograph had a worse clinical course. These patients might benefit from more aggressive therapy. Our study suggests that predicting the disease severity based on admission chest radiograph may be a useful tool allowing early intervention in the disease course to prevent clinical deterioration and shorten length of stay. doi:10.4021/ijcp4w
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".