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Record W7133043093

Respiratory Symptoms, Health-Related Quality of Life, Physical Activity and Symptom Management in Ehlers-Danlos Syndromes and Generalized-Hypermobility Spectrum Disorders: A Mixed-Methods Study

2023· dissertation· W7133043093 on OpenAlexaff
Noor Al Kaabi

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRespiratory systemQuality of life (healthcare)Physical activityHypermobility (travel)Respiratory diseaseActivities of daily livingRespiratory physiology
DOInot available

Abstract

fetched live from OpenAlex

Ehlers-Danlos Syndromes (EDS) and Generalized Hypermobility Spectrum Disorders (G-HSD) are connective tissue disorders with multi-systemic manifestations. Individuals with EDS or G-HSD experience respiratory symptoms which may have a negative impact on health-related quality of life (HRQL), functional capacity, and fatigue. Notably, unmet symptom management needs are reported by patients. The overarching goals of this thesis were to describe respiratory symptoms in EDS and G-HSD and their association with key patient-reported outcomes and identify the respiratory symptom experiences and management needs. We found that dyspnea was associated with a lower physical component of HRQL, lower functional capacity, higher fatigue severity, and contributed to a cycle of inactivity. Respiratory symptoms (e.g., dyspnea) were a barrier to physical activity and limited daily activities. Respiratory symptoms were defined by a shared characteristic of ‘invisibility’ and were under-recognized in clinical care. Coherent care plans were emphasized by patients as beneficial in improving their respiratory care.

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.050
GPT teacher head0.456
Teacher spread0.406 · 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 designQualitative
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

Citations0
Published2023
Admission routes1
Has abstractyes

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