Fibrinogen and the prediction of residual obstruction manifested after pulmonary embolism treatment
Bibliographic record
Abstract
Residual pulmonary vascular obstruction (RPVO) and chronic thromboembolic pulmonary hypertension (CTEPH) are both long-term complications of acute pulmonary embolism, but it is unknown whether RPVO can be predicted by variants of fibrinogen associated with CTEPH.We used the Akaike information criterion to select the best predictive models for RPVO in two prospectively followed cohorts of acute pulmonary embolism patients, using as candidate variables the extent of the initial obstruction, clinical characteristics and fibrinogen-related data. We measured the selected models' goodness of fit by analysis of deviance and compared models using the Chi-squared test.RPVO occurred in 29 (28.4%) out of 102 subjects in the first cohort and 46 (25.3%) out of 182 subjects in the second. The best-fit predictive model derived in the first cohort (p=0.0002) and validated in the second cohort (p=0.0005) implicated fibrinogen Bβ-chain monosialylation in the development of RPVO. When the derivation procedure excluded clinical characteristics, fibrinogen Bβ-chain monosialylation remained a predictor of RPVO in the best-fit predictive model (p=0.00003). Excluding fibrinogen characteristics worsened the predictive model (p=0.03).Fibrinogen Bβ-chain monosialylation, a common structural attribute of fibrin, helped predict RPVO after acute pulmonary embolism. Fibrin structure may contribute to the risk of developing RPVO.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".