Characteristics, treatment and outcomes of nontuberculous mycobacterial pulmonary disease after allogeneic haematopoietic stem cell transplant
Bibliographic record
Abstract
Allogeneic haematopoietic stem cell transplant (alloHCT) recipients are at risk for a variety of opportunistic infections. While typical bacterial infections occur most frequently, nontuberculous mycobacteria (NTM) are becoming increasingly recognised as pathogens after alloHCT [1]. The prevalence of NTM pulmonary disease (NTM-PD) is increasing worldwide, with a rise in Ontario's period prevalence from 29.3 per 100 000 in 1998–2002 to 41.3 per 100 000 in 2006–2010 [2]. Likewise, the incidence of NTM-PD post alloHCT increased from 0.11–0.23% [3, 4] in early studies to 2.7–3.15% in recent investigations [5, 6]. Despite the incidence of NTM-PD after alloHCT being far higher than in the general population [6], there are few reports addressing management and treatment outcomes of NTM-PD after alloHCT. We reviewed the experience with NTM-PD among alloHCT recipients at our institution to better characterise clinical features and treatment outcomes. Nontuberculous mycobacterial pulmonary disease in allogeneic haematopoietic stem cell transplant recipients
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".