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A systematic survey of the methods literature on the reporting quality and optimal methods of handling participants with missing outcome data for continuous outcomes in randomized controlled trials

2017· review· en· W2621147310 on OpenAlexaff
Akram Alyass, Thuva Vanniyasingam, Behnam Sadeghirad, Iván D. Flórez, Sathish Chandra Pichika, Sean A. Kennedy, Ulviya Abdulkarimova, Yuan Zhang, Tzvia Iljon, Gian Paolo Morgano, Luis Enrique Colunga‐Lozano, Fazila Aloweni, Luciane Cruz Lopes, Juan José Yepes-Núñez, Yutong Fei, Li Wang, Lara A Kahale, David Meyre, Elie A. Akl, Lehana Thabane, Gordon Guyatt

Bibliographic record

VenueJournal of Clinical Epidemiology · 2017
Typereview
Languageen
FieldMathematics
TopicAdvanced Causal Inference Techniques
Canadian institutionsUniversity of TorontoMcMaster UniversityImpact
Fundersnot available
KeywordsMissing dataRanking (information retrieval)Imputation (statistics)Randomized controlled trialData qualityStatisticsType I and type II errorsData collectionSystematic reviewMeta-analysisComputer scienceMedicineMEDLINEMathematicsMachine learningOperations management

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

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)0.871
metaresearch head score (Gemma)0.994
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad)
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.349
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.8710.994
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.1030.005
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.002
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 head0.969
GPT teacher head0.798
Teacher spread0.171 · 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; both teacher heads agree on what is shown here.

Study designSystematic review
DomainMethods
GenreMethods

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

Citations48
Published2017
Admission routes1
Has abstractno

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