Reply to Ku et al
Bibliographic record
Abstract
To the Editor—We thank Dr Ku and colleagues for their interest in our study [1] and for contributing to existing data regarding the emerging association between influenza and invasive aspergillosis. Ku et al report influenza-associated invasive pulmonary aspergillosis (IAIPA) in 11 of 89 patients in 3 Taiwanese centers with influenza, including 8 of 52 (15.4%) in the intensive care unit (ICU). Further analysis showed that although the total incidence of IAIPA was similar in 2015–2016 and in 2018, there was marked variation in incidence of IAIPA among the subgroup of patients with influenza A(H1N1) in different years [2]. On first glance, the rate of IAIPA appears to be considerably higher than what we observed in Alberta, Canada (7.2%) [1], and similar to what has been reported elsewhere in Europe, notably in the Netherlands and Belgium (19%) [3]. However, some caution is warranted in interpreting these data. As the authors point out, their definitions of IAIPA were different from those used in other studies, limiting comparisons. First, clinical features of patients are not described, and so it is unclear whether patients met clinical or radiographic criteria for the diagnosis as published by Schauwvlieghe et al [3]. Second, mycological criteria for IAIPA are lacking in 3 of 11 patients: 2 patients in whom Aspergillus was cultured from sputum, and a third with elevated galactomannan in an endotracheal aspirate. Additional data are needed to describe the epidemiology of IAIPA, but comparisons of incidence rates in different settings could be facilitated by use of consistent research definitions. The most widely used research definitions for invasive fungal disease were recently revised by the European Organization for the Research and Treatment of Cancer (EORTC) and the Mycosis Study Group Education and Research Consortium [4]. Except when proven by histopathology, mycological data alone lack sufficient specificity for diagnosing invasive aspergillosis, necessitating the consideration of host criteria and clinical syndromes. The current EORTC/Mycosis Study Group guidelines do not consider antecedent influenza infection as a host criterion [4], and ICU patients with invasive aspergillosis may lack classic host factors. For this reason, Blot et al published an alternative algorithm (known as AspICU) for the diagnosis of invasive pulmonary aspergillosis (IPA) in this population [5], and Schauwvlieghe et al modified this to study IAIPA in ICU patients [3]. It is likely that patients with milder forms of influenza—that is, patients not requiring intensive care for respiratory failure—and without classic host factors can still develop IPA. However, studying this phenomenon may be difficult because AspICU criteria and published modifications don’t apply outside ICUs, and no current research definitions could capture this group. If further data suggest this occurs frequently, this may prompt modification to future research definitions. In an editorial accompanying our report, Rijnders et al have advised that a new consensus research definition of IAIPA is forthcoming [6]. We welcome the publication of refined standardized definitions to ensure that researchers studying the phenomenon of IAIPA in different settings are comparing the same thing to one another. Such studies are much needed to make sense of the apparent wide variation in the incidence of IAIPIA across geographic settings, and, as we found, across seasons. Potential conflicts of interest. The authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest.
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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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.136 | 0.062 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".