Bibliographic record
Abstract
If William Osler were alive today he would no doubt remark on the fundamental change in the nature of the disease that he originally described. Staphylococcal endocarditis in injection drug users is now the dominant form of the disease in many urban communities where there is a high incidence of injection drug use and homelessness. At our institution (a tertiary care, university affiliated hospital in inner Vancouver), 63% of 116 hospitalisations between 1994 and 2000 for infective endocarditis (IE) were in injection drug users. Right sided endocarditis accounts for 10% of all IE in population based surveys1 and a higher proportion of IE in injection drug users. Modern echocardiographic techniques have considerably augmented our ability to diagnose IE and to understand its natural history. Despite this, there are many areas in which our understanding of right sided IE remains incomplete. Right sided IE has a significant morbidity and mortality that adds a notable economic burden to stretched inner city health care facilities. The challenges of caring for this population of patients should not be underestimated and demands a logical and coordinated approach among care providers and physicians from a variety of specialties. This article reviews aspects of the epidemiology, clinical features, diagnosis, and treatment of right sided IE in injection drug users. Although right sided IE may occur in association with congenital heart disease and instrumentation of the right heart, it is overwhelmingly a disease of injection drug users, at least in western populations. Among injection drug users presenting with fever, 13% will have echocardiographic evidence of IE.2 If injection drug users with bacteraemia from an inner city demographic are considered, up to 41% will have evidence of IE.3 The pathogenic mechanisms that explain the increased prevalence of right sided IE in injection drug users are not …
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".