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Record W2799699001 · doi:10.1007/s11136-018-1861-0

A systematic review of the quality of reporting of simulation studies about methods for the analysis of complex longitudinal patient-reported outcomes data

2018· review· en· W2799699001 on OpenAlexafffund
Aynslie Hinds, Tolulope T. Sajobi, Véronique Sebille, Richard Sawatzky, Lisa M. Lix

Bibliographic record

VenueQuality of Life Research · 2018
Typereview
Languageen
FieldMathematics
TopicStatistical Methods and Bayesian Inference
Canadian institutionsTrinity Western UniversityProvidence Health CareUniversity of CalgaryWestern UniversityUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsQuality of Life ResearchPublic healthQuality (philosophy)Longitudinal dataSystematic reviewMEDLINEMedicinePsychologyData scienceComputer scienceData miningNursingPolitical science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Reporting · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
gptMetaresearch
Domain: Reporting · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.114
metaresearch head score (Gemma)0.480
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.886
Threshold uncertainty score0.601

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1140.480
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0160.020
Bibliometrics0.0110.009
Science and technology studies0.0010.003
Scholarly communication0.0060.005
Open science0.0040.004
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.937
GPT teacher head0.756
Teacher spread0.180 · 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

Labeled directly by 2 models reading the full record.

Study designSystematic review
DomainReporting
GenreReview

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

Citations9
Published2018
Admission routes2
Has abstractno

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