Symptomatic subsegmental pulmonary embolism: to treat or not to treat?
Bibliographic record
Abstract
The introduction of computed tomographic pulmonary angiography and its recent increasing availability has led to a significant rise in its use to help clinicians diagnose acute pulmonary embolism (PE). This has led to a significant increase in the incidence of PE diagnoses. Simultaneously, the case fatality rate of acute PE has been decreasing and no significant change in its mortality has been noted, suggesting that the additional PE diagnoses are less severe and these patients might not benefit from anticoagulation therapy. This also seems to be correlated with an increase in the diagnosis of PE localized in the subsegmental pulmonary arteries (subsegmental pulmonary embolism [SSPE]). The clinical importance of SSPE is unclear. Whereas some studies have shown that it might be reasonable to manage patients with SSPE without anticoagulation, others have not. Although the current medical literature is limited, it suggests that a subgroup of patients with SSPE might be safely managed without the use of anticoagulant therapy. Current clinical practice guidelines suggest that clinicians take an individualized approach after carefully assessing the risk/benefit ratio for patients with SSPE and negative leg limb ultrasonography results. Prospective studies are ongoing and results are eagerly awaited to help tailor the management of this patient population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".