Bibliographic record
Abstract
Pericardial disease in the developing world is dominated primarily by effusive and constrictive syndromes and contributes to the acute and chronic heart failure burden in many regions. The confluence of geography (location in the tropics), a significant burden of diseases of poverty and neglect, and a significant contribution of communicable diseases to the general burden of disease is reflected in the wide etiological spectrum of causes of pericardial disease. The prevalence of Mycobacterium tuberculosis in particular, is high throughout much of the developing world where it is the most frequent and important cause of pericarditis and is associated with significant morbidity and mortality. Acute viral/idiopathic pericarditis, which is the primary manifestation of pericardial disease in the developed world is believed to occur significantly less frequently in the developing world. Although diagnostic approaches and criteria to establish the diagnosis of pericardial disease are similar throughout the globe, resource constraints such as access to multimodality imaging and hemodynamic assessment are a major limitation in much of the developing world. These important considerations significantly influence the diagnostic and treatment approaches, and outcomes related to pericardial disease.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".