Association between pre-treatment computed tomography findings and post-treatment persistent decrease in lung perfusion blood volume
Bibliographic record
Abstract
The purpose of this study was to evaluate pre-treatment CT findings in patients with acute pulmonary embolism (PE) and determine the imaging findings associated with residual hypoperfused segments in post-treatment lung perfused blood volume (LPBV). We evaluated 91 patients with acute PE who underwent dual-energy CT before and after treatment. The location of thrombi (proximal or distal) and patency of the pulmonary artery (occlusive or non-occlusive) were recorded using pre-treatment computed tomography pulmonary angiography (CTPA). Residual hypoperfusion was defined as a perfusion-decreased area seen in both the pre- and post-treatment LPBVs. The association of the location of the thrombus and vascular patency of pre-treatment CTPA with residual hypoperfusion on a segmental and patient basis was examined. In the segment-based analysis, the proportion of residual hypoperfusion in the proximal group was significantly higher than that in the peripheral group (33/125 [26.4%] vs. 9/87 [10.3%], P = 0.004). Patient-based analysis also showed that the proportion of residual hypoperfusion in patients with pre-treatment proximal thrombus was significantly higher than those without (16/42 [38.1%] vs. 3/25 (12.0%); P = 0.022). Pre-treatment vascular patency was not significantly associated with residual hypoperfusion (P > 0.05). Therefore, careful follow-up is necessary, especially in patients with proximal thrombi.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".