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Record W2613976121 · doi:10.1093/sleep/zsx060

Reply to: “Periodic Limb Movements During Sleep and White Matter MRI Hyperintensity in Minor Stroke or TIA”

2017· letter· en· W2613976121 on OpenAlexafffund
Mark I. Boulos, Ryan T. Muir, Fuqiang Gao, Andrew Lim, Richard H. Swartz, Sandra E. Black, Arthur S. Walters, Brian J. Murray

Bibliographic record

VenueSLEEP · 2017
Typeletter
Languageen
FieldMedicine
TopicRestless Legs Syndrome Research
Canadian institutionsHealth Sciences CentreToronto Sleep InstituteOntario Stroke NetworkSunnybrook Health Science CentreHeart and Stroke FoundationUniversity of Toronto
FundersCanadian Institutes of Health ResearchCanadian Stroke NetworkHeart and Stroke Foundation of Canada
KeywordsHyperintensityMinor strokeStroke (engine)MedicineWhite matterSleep (system call)Physical medicine and rehabilitationCardiologyMagnetic resonance imagingLeukoaraiosisPsychologyRadiologyPhysics

Abstract

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We thank Manconi et al.1 for their interest in our publication.2 We think that several methodological differences explain the discrepancy between the results of Manconi et al.1 and our work.2 First, the white matter hyperintensity (WMH) volumes reported by Manconi et al. (in Tables 1 and 2) were unexpectedly low (median of 1.3–1.8 cm3), which is substantially lower than that which has been previously reported in similarly-aged normal controls3 (mean of 3.64 cm3) and in two cohorts of patients with minor stroke/transient ischemic attack (TIA) (median of 4.65 cm3 in one study4 and a median of 5.08 cm3 in another cohort with transient symptoms5). This may have been because they only scored WMH volumes over the non-affected hemisphere, thereby providing an incomplete picture of the total WMH volume in the patients they examined. Regardless, the inclusion of such low WMH volumes likely gave rise to a floor effect and made it difficult for the authors to demonstrate any relationship with the periodic limb movement (PLM) index. In comparison, we used the Age Related White Matter Change (ARWMC) Scale, a well-validated rating scale,6 which is strongly correlated to WMH volume and furthermore to cognitive function.3 Second, details surrounding the scoring of PLMs are not provided by Manconi et al.1 or in the manuscript that details the methodology of the SAS CARE study.7 For example, it is unclear whether a nasal pressure transducer was used to eliminate respiratory events related to upper airway resistance. In the work reported by Manconi et al.,1 the baseline PLM index was significantly related to the apnea-hypopnea index (AHI) and arousal index, suggesting that underlying sleep-disordered breathing may have played an important role in the scored PLMs and thereby could have confounded any potential relationship with WHM volume. Next, important differences are present between the characteristics of the cohort we studied2 and that reported by Manconi et al.1 The median age of the patients with a PLM index ≥ 5/h (59.7 years) was 10 years younger in the report by Manconi et al.1 compared to the median age observed in our patients with a PLM index ≥ 5/h (70.0 years)2; younger patients are known to have less WMH burden and are likely more resilient to the fluctuations in blood pressure and sympathetic tone that are thought to occur with PLMs. Furthermore, the baseline median AHI in the cohort reported by Manconi et al.1 was 15.4, which was substantially greater than the median AHI of 2.5 observed in our study.2 Other important characteristics of the cohort examined by Manconi et al.1 are not provided. For example, gender, the number of patients that presented with TIA (vs. minor stroke), associated medical co-morbidities, and presence of pre-stroke/TIA PLM triggers (such as Restless Legs Syndrome) are not reported.1 Overall, the two studied cohorts are very different in their underlying characteristics. Given the methodological issues noted above and the major differences between the two cohorts studied, we disagree that the study by Manconi et al.1 is a true replication of our work. As recently reported by our research group,8 the current evidence is limited but does suggest that PLMs are a prognostic factor for incident vascular events and mortality. Other studies also support a close association between PLMs and stroke,9,10 as does our present study.2 On the other hand, the work of Koo et al.11 found a relationship between PLMs and all-cause cardiovascular disease including stroke but could not find a relationship between PLMs and stroke when stroke was studied as an isolated entity, a result more in alignment with that reported by Manconi et al.1 Further work is needed to more fully understand the relationship between PLMs and stroke and to resolve the current contradictions in the literature. Future randomized controlled trials to study the impact of treating PLMs on vascular outcomes could be one such approach. During this study, Dr. Boulos was supported by a Focus on Stroke 2010 Research Fellowship, which was funded by the Heart and Stroke Foundation of Canada, the Canadian Stroke Network and the Canadian Institutes of Health Research; he was also supported by fellowship funding from the Canadian Partnership for Stroke Recovery. Dr. Swartz is supported by the Heart and Stroke Foundation of Canada New Investigator Award and Barnett Award, and this work was supported by operating grant funding from the Heart and Stroke Foundation and the Canadian Institute of Health Research. Dr. Lim has engaged in consulting activities for UCB S.A. and Merck & Co. Inc. Dr. Walters has served as a consultant on RLS to UCB Pharma and MundiPharma, and has also received grant funding for investigator-initiated projects from both companies. Dr. Walters has also participated in a study initiated by UCB. All other authors report no conflicts of interest. Disclosure of any off-label or investigational use: None.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.062
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0620.032
Insufficient payload (model declined to judge)0.0070.007

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.

Opus teacher head0.029
GPT teacher head0.299
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2017
Admission routes2
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