Bibliographic record
Abstract
Improvements in anticancer treatments, the ability to modify myelosuppression profiles, greater duration and intensity of immunosuppression, and the variety of available antimicrobial therapies have influenced the spectrum of pathogens associated with invasive fungal infection complicating treatment of hematological malignancies and hematopoietic stem cell transplantation. The approaches to the management of these infections encompass strategies of prevention for all those at risk, preemptive therapy based upon surrogates of infection before the onset of clinical disease, empirical therapy for patients with clinical evidence of early disease, and directed or targeted therapy for infected patients with established disease. Chemoprophylaxis is effective if applied to the highest risk patients over the duration of the risk. Preemptive strategies, while promising, have yet to be validated and linked to reliably predictive non-microbiological diagnostic techniques. Empirical antifungal therapy, as it is currently applied, now seems questionable. Patients with probable or proven invasive fungal infection still have suboptimal outcomes despite the availability of promising anti-fungal agents. Strategies examining the concept of dose-intensity and combination regimens require careful study and cannot yet be regarded as an acceptable standard of practice.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".