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Record W3195832724 · doi:10.21203/rs.3.rs-806086/v1

Concordance Between The Schedule for The Evaluation of Individual Quality of Life – Direct Weighting (SEIQoL-DW) and The EuroQoL-5D (EQ-5D) Measures of Quality of Life Outcomes in Adults With X-linked Hypophosphatemia.

2021· preprint· en· W3195832724 on OpenAlexaff
Ravi Jandhyala

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsThe King's University
Fundersnot available
KeywordsQuality of life (healthcare)Baseline (sea)ConcordancePsychologyConstruct (python library)WeightingScheduleClinical psychologyMedicineGerontologyComputer scienceInternal medicinePsychotherapist

Abstract

fetched live from OpenAlex

Abstract BACKGROUNDAccurate measurement of any constructs in clinical studies is of critical importance, especially if the adoption of an intervention relies on detecting a significant treatment effect where one exists. Under Neutral theory, the amount of relevant and irrelevant indicators selected to operationalise the construct contribute equally to the accuracy of the observation. The Neutral or accurate observation is achieved by observing all relevant indicators only. Generic QoL instruments such as EQ-5D are increasingly being accepted as inaccurate, especially in rare diseases, based on the relevance of their indicators. QoL is a construct that embodies a patient's subjectivity, individuality, and local circumstances at measurement. SEIQoL-DW is an instrument designed to respect these characteristics of QoL through eliciting indicators or cues directly from the subject along with the proportion of the overall QoL they contribute. EQ-5D and SEIQoL can therefore be considered as being at opposing ends of accuracy in QoL measurement. XLH is a hereditary, progressive, rare disease characterised by phosphate wasting, affecting both children and adults and impacting their QoL. The purpose of this study was to observe if any change in QoL of adult XLH patients were detectable using EQ-5D, SEIQoL eliciting new cues at each visit, and SEIQoL administering baseline cues overall visits (thereby silencing its time-dependency) versus baseline over 12 months. Secondly, to explore any association between the three sets of observations.RESULTSAll quality of life scores were observed to decrease from baseline by 13.36%, 7.32%, and 2.7% based on SEIQoLvisit_cues, SEIQoLbaseline_cues, and EQ-5D assessments, respectively. The decrease in the quality of life scores was only statistically significant (p=0.037) for SEIQoLvisit_cues. Beyond the baseline visit, the only highly positive and statistically significant pairwise association was between SEIQoLvisit_cues and SEIQoLbaseline_cues at M6 (ρ=0.782, P value<0.05) and M9 (ρ=0.879, P value<0.05).CONCLUSIONSEQ-5D and SEIQoLbaseline_cues failed to detect the same statistically significant decrease in QoL observed by SEIQoLvisit_cues. Both sets of SEIQoL observations were more closely associated with each other than with EQ-5D. Observing constructs such as QoL in rare diseases benefit from a Neutrality in indicator selection and respecting variation in dominance of various indicators over time.

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)0.008
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.367
GPT teacher head0.464
Teacher spread0.097 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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