Comment on: Extracorporeal membrane oxygenation for acute lung injury in idiopathic inflammatory myopathies—a potential lifesaving intervention: Reply
Bibliographic record
Abstract
Dear Editor, We have read with interest the matters arising letter by Bay et al. [1] in response to our case series ‘Extracorporeal membrane oxygenation for acute lung injury in idiopathic inflammatory myopathies—a potential lifesaving intervention’ [2]. We thank the authors for sharing their experience with and insights into the use of extracorporeal membrane oxygenation (ECMO) in idiopathic inflammatory myopathies–associated interstitial lung disease (IIM-ILD). As pointed out by the authors, our population was indeed heterogeneous, although with a predominance of anti-MDA5–positive patients (n = 15, 68%). Given the lack of data on ECMO use in IIM-ILD, our goal was to report as many cases as possible for situations where ECMO has been used in IIM-ILD and their outcomes. Stratification on autoantibody status and other variables would have been very informative, but it will require larger cohorts of patients. We appreciate Bay et al. [3] drawing attention to the subgroup of patients with anti-MDA5 and rapidly progressive ILD, as this is a very challenging group to treat and whose prognosis can be poor. As mentioned in their comment and in our discussion, lung transplantation is increasingly used in that population of severely ill patients with good results. However, some centres may have limited access to lung transplantation, and the description of anti-MDA5–positive patients with rapidly progressive ILD successfully bridged to recovery with ECMO is of importance. The authors also raise important questions about the optimal management of IIM with rapidly progressive ILD, particularly in the presence of anti-MDA5. More insight is needed to identify ideal regimens for immunosuppression in IIM-ILD and good candidates for ECMO. Newer therapies for IIM-ILD (i.e. Janus kinase inhibitors, obinutuzumab, daratumumab, CAR-T cells) will increase the treatment options for these patients and hopefully improve their outcomes. In our case series, 36% of patients received rituximab, but newer therapies were not widely used during the look-back period (2000–2020). Obinutuzumab, daratumumab, and CD19 CAR-T cells became therapeutic options for CTD-ILD only recently, and they are exciting options for the future, although to date there are very limited data available on their use in IIM [4, 5]. Finally, the authors are highlighting the importance of large collaborative efforts to address crucial unmet needs in IIM-ILD management, and we are in complete agreement with them. We propose that researchers should leverage existing platforms and international registries such as the MYONET registry (https://www.myonet.info/) to accelerate research in IIM-ILD. In parallel, working groups with a special focus on ILD within existing networks of IIM specialists (e.g. MIHRA; https://mihrafoundation.org/) have the potential to mobilize experts, define research priorities and facilitate collaborations in the field. No new data were generated or analysed in support of this article. No specific funding was received from any funding bodies in the public, commercial or not-for profit sectors to carry out the work described in this manuscript. Disclosure statement:The authors have declared no 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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.041 | 0.036 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".