Recombinant Human Granulocyte-Macrophage Colony-Stimulating Factor (rhu GM-CSF) as Adjuvant Therapy for Invasive Fungal Diseases
Bibliographic record
Abstract
Abstract Background Sargramostim (yeast-derived, glycosylated recombinant human granulocyte-macrophage colony-stimulating factor [rhu GM-CSF]) augments innate and adaptive immune responses and accelerates hematopoietic recovery of chemotherapy-induced neutropenia. However, considerably less is known about its efficacy as adjunctive immunotherapy against invasive fungal diseases (IFDs). Methods The clinical courses of 15 patients with pediatric malignancies and IFDs treated adjunctively with sargramostim at a single institution were analyzed in a retrospective cohort review. Further, a systematic review of published reports of rhu GM-CSF for IFDs was also conducted. Results Among 65 cases, 15 were newly described pediatric patients and 50 were previously published cases of IFDs treated with rhu GM-CSF. Among the newly reported pediatric patients, IFDs were caused by Candida spp., Trichosporon sp., and molds (Aspergillus spp., Rhizopus sp., Lichtheimia sp., and Scedosporium sp). Twelve (80%) were neutropenic at baseline, and 12 (80%) were refractory to antifungal therapy. Among 12 evaluable patients, the overall response rate was 92% (8 [67%] complete responses, 3 [25%] partial responses, and 1 [8%] stable). Treatment is ongoing in the remaining 3 patients. Among 50 published cases (15 Candida spp., 13 Mucorales, 11 Aspergillus spp., 11 other organisms), 20 (40%) had baseline neutropenia and 36 (72%) were refractory to standard therapy before rhu GM-CSF administration. Consistent with responses in the newly reported patients, the overall response rate in the literature review was 82% (40 [80%] complete responses, 1 [2%] partial response, and 9 [18%] no response). Conclusions Sargramostim may be a potential adjunctive immunomodulator for selected patients with hematological malignancies and refractory IFDs.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".