1Dear Delegates, I have the privilege to welcome you to an
Bibliographic record
Abstract
Interactive Case Based discussion on Systemic Fungal Infections. Fungal infections are an increasing cause of morbidity and mortality in immunocompromised and critically ill patients. As we treat an increasingly aging population, prolong life with immunosuppressive therapy for a variety of disorders and administer broad spectrum antibiotics, the risk of fungal infections increases. An increasing incidence of invasive candidiasis, aspergillosis and zygomycosis has been reported from India. As we do not have a subspeciality of infectious diseases, an Internal Medicine specialist is often called upon to give advice regarding the diagnosis and treatment of suspected or proven fungal infection. It is therefore important to know the diagnostic criteria and therapeutic options for treating such disorders. There is a paucity of good mycological laboratories in the country. Fortunately the options for therapy have increased in the past decade but many clinicians may not be familiar with these newly licensed agents. Most of the evidence based guidelines for management of systemic fungal infections are based on experience of patients of acute leukemia and bone marrow transplantation due to the high incidence of fungal infections in this population. These can provide a framework for management in general medical patients who acquire similar infections. In this symposium we have invited a number of eminent authorities from India and Canada, to provide a
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.149 | 0.042 |
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".