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Record W2342633757 · doi:10.1080/17843286.2016.1173941

Noninvasive ventilation in amyotrophic lateral sclerosis: effects on sleep quality and quality of life

2016· article· en· W2342633757 on OpenAlexaboutno aff
Eva Vandoorne, Bart Vrijsen, Catharina Belge, Dries Testelmans, Bertien Buyse

Bibliographic record

VenueActa Clinica Belgica · 2016
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInterquartile rangeAmyotrophic lateral sclerosisQuality of life (healthcare)Physical therapyPittsburgh Sleep Quality IndexEpworth Sleepiness ScaleSleep qualityInternal medicineDiseasePolysomnographyPsychiatryInsomnia

Abstract

fetched live from OpenAlex

OBJECTIVES: Little is known about the effects of noninvasive ventilation (NIV) on sleep quality in amyotrophic lateral sclerosis (ALS). We aim to evaluate the long-term effects of NIV on sleep quality and quality of life in patients with ALS. METHODS: In this prospective observational study, 13 ALS patients were followed for one year after initiating NIV. We evaluated sleep quality, quality of life and functional status with several questionnaires: Epworth sleepiness Scale (ESS), Pittsburg sleep quality index (PSQI), Short Form 36 Health Questionnaire (SF-36), McGill Quality of Life questionnaire (McGillQoL) and revised Amyotrophic Lateral Sclerosis Functional Rating Scale scores (ALSFRS-R). RESULTS: at start of NIV treatment was 48 (43-52) mmHg (median, IQR). During the one-year follow-up period, a significant decrease in the ALSFRS-R was observed. The impact of NIV in a short term (1 month) revealed a statistically significant decrease in ESS, decrease in total PSQI and of four PSQI subscales and improvement of almost all subscales of the McGill questionnaire. Long-term analyses (9 months to 1 year) revealed that amelioration in ESS and total PSQI was sustained. CONCLUSION: We conclude that accurately titrated NIV in ALS patients can stabilize sleep quality and quality of life for at least one year, despite significant disease progression.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.624

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.145
GPT teacher head0.391
Teacher spread0.246 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations35
Published2016
Admission routes1
Has abstractyes

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