Report from the 4th Advances Against Aspergillosis Conference
Bibliographic record
Abstract
Traditionally, the patients believed to be at highest risk of invasive aspergillosis (IA) are those who are neutropenic due to chemotherapy for hematological malignancy or those undergoing allogeneic hematopoietic stem cell transplantation. However, emerging data show that other patients are vulnerable to IA, even though some are not classically immunocompromised. These include: solid organ transplant recipients; patients with TB, chronic obstructive pulmonary disease and patients in the intensive care unit for other reasons. The conference highlighted the diagnostic and therapeutic challenges facing physicians treating this diverse group, not least of which include the unreliable estimates of IA incidence due to poor surveillance and inadequate data collection. Moreover, although there is now considerable experience of IA in neutropenic patients, much less is known about the management of those who are non-neutropenic. Nevertheless, approaches that have proven effective in neutropenic patients may also benefit others in this growing population. The meeting, attended by more than 500 delegates from almost 50 countries, also provided the opportunity to hear how basic scientific research may improve the understanding of the pathogenic mechanisms and therapy of IA.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 0.018 |
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".