Systemic Lupus Erythematosus Presenting as Pulmonary Embolism After Liposuction: A Clinical Conundrum
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) is an autoimmune disorder with a wide array of presentations and a predilection to affect women of certain ethnic backgrounds. The hallmark of the disease is multisystem involvement, dispersed in time and severity. Usual pulmonary involvement includes pleuritis, pleural effusions, pneumonitis, shrinking lung syndrome, pulmonary hypertension, and alveolar hemorrhage. Pulmonary embolism (PE) is a relatively unusual presentation of SLE. We present the case of a 28-year-old Hispanic female who presented with shortness of breath and chest pain after liposuction and was found to have a PE. Fat embolism was ruled out. The absence of overt signs and symptoms and traditional risk factors prompted a fragmentary workup. This led to the detection of antibodies sensitive and specific for SLE, in the absence of overt signs and symptoms. We revive the concept of latent lupus, a condition construed as early lupus. Since our patient was lost to follow up, we were unable to complete workup for SLE, but firmly suspect direct causation between SLE and PE. Further studies are needed to establish pathogenesis in order to facilitate early diagnosis and prevent morbidity and mortality from PE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".