Dear Dr. Uzawa,

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Academic Editor

PLOS ONE

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“AU has received honoraria from Alexion Pharmaceuticals and Argenx. SS received speakers’ fees from Alexion Pharmaceuticals, the Japan Blood Products Organization, Asahi Kasei Medical and Argenx and participated on advisory board meeting of Alexion Pharmaceuticals and Argenx. SK has received Research Grant on Intractable Diseases (Neuroimmunological Diseases) from the Ministry of Health, Labour and Welfare of Japan (20FC1030) and honoraria from CSL Behring. TK reports honorarium for lectures from Alexon Pharmaceuticals.

MPT reports unrestricted research grants from Japan Blood Products Organization, Astellas Pharma, Mitsubishi Tanabe Pharma and Pfizer, outside the submitted work, and has served as a paid Consultant for Alexion, Argenx, Sanofi, and UCB Pharma and received honorarium for lectures from Argenx, Alexion Pharmaceuticals and UCB Pharma. MM has received speaker honoraria from Argenx, Asahi Kasei Medical, Alexion Pharmaceutical and participated on advisory board meeting of Alexion Pharmaceutical. KU has served as a paid Consultant for UCB Pharma, Janssen Pharma, Horizon Therpeutics (Viela Bio), Chugai Pharma, Hanall BioPharma and Mitsubishi Tanabe Pharma, and has received speaker honoraria from Argenx, Alexion Pharmaceuticals, UCB Pharma, and the Japan Blood Products Organisation. Other authors declare no financial or other conflicts of interest.

YN has received speaker honoraria from argenx, Alexion Pharmaceuticals, Japan Blood Products Organization, and UCB Pharma.”

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: Comment#1

The introduction effectively outlines the pathophysiology, classification, and assessment tools for Myasthenia Gravis (MG), making it informative for readers. Highlighting the challenges in defining Minimal Manifestation (MM) status aligns well with the study's objective of standardization, making the research question clear. Also, the transition from general MG characteristics to the specific issue of MM definition is well-structured.

Comment#2

"However, there is no clear definition to identify minimal muscle weakness that is clinically important, which can result in variability in how physicians interpret and apply the MM criteria."

Comment: The variability in MM criteria is a key gap, but the impact of this inconsistency on clinical decision-making should be more explicitly stated. Does it lead to differences in treatment approaches, misclassification of patients, or difficulties in research comparability?

Comment#3

"The quantitative MG (QMG) scale, the MG activities of daily living (MGADL) score, the MG composite (MGC) scale are widely used to evaluate the clinical symptoms of MG patients."

Comment: The sentence is dense and would benefit from breaking down each scale's purpose briefly (e.g., QMG for objective severity, MGADL for daily function, MGC as a composite measure).

Comment#4

Use "Myasthenia Gravis (MG)" at first mention, then "MG" thereafter for consistency.

Reviewer #2: This study developed and assessed machine learning models to predict clinical goal achievement in myasthenia gravis. I commend the authors for utilizing artificial intelligence to help monitor management in this patient population. Some comments for consideration:

1. Throughout the results (AUROC, sensitivity, specificity) results are given without 95% CI. I think it would be valuable to assess the variation in these results based on the 95% CI range and the authors should include them for each result, both in the abstract and the main results.

2. The authors state that the clinical status was assessed by physicians expert in MG treatment- were these physicians primary care, rheumatologists, or any other?

3. What were the baseline characteristics of patients in the 2015 validation cohort? A table summarizing them, perhaps in the supplement, will enhance the findings in the study.

Reviewer #3: This manuscript presents a diagnostic model that predicts NM or better status in MG patients. The model achieves good performance on both the training and validation datasets. However, several points need to be addressed before the manuscript can be considered for publication:

1. The authors should provide a thorough review of existing models that predict NM status in MG patients.

2. If other models for NM prediction exist, the authors need to clearly highlight the advantages of their approach in comparison.

3. (Line 249): The legend for Figure 2 appears after the legend for Figure 3. Please correct the order.

4. (Line 250): Change "he H matrix" to "The H matrix".

5. (Line 261): The authors should report both training and test results for all four models, including SVM, logistic regression, random forest, and Naive Bayes.

6. (Line 261): Since a 5-fold cross-validation was used, the variability across the five folds should be reported.

7. (Line 274): Similarly, for the ensemble model, both training and test performance should be shown, along with the variability across the five cross-validation folds.

8. (Line 277): Given that five iterations were conducted, was an ensemble model generated for each iteration? If so, how were the five ensemble models applied to the external validation dataset? Please clarify.

9. Figures 4 and 5: The ROC curves shown in Figures 4 and 5 use inconsistent font sizes, legend locations, and titles. Please ensure uniform formatting.

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what does this mean? ). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes:  Ahmed Kamal Siddiqi

Reviewer #2: No

Reviewer #3: No

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Predicting achievement of clinical goals using machine learning in myasthenia gravis

PONE-D-24-57934R1

Dear Dr. Uzawa,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Pinyi Lu, Ph.D.

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #2: The authors have adequately addressed my comments and revised the manuscript accordingly. The manuscript is acceptable for publication.

Reviewer #3: (No Response)

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what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #2: No

Reviewer #3: No

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