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Record W2768349508 · doi:10.1136/bjsports-2017-098520

Distinguishing between causal and non-causal associations: implications for sports medicine clinicians

2017· editorial· en· W2768349508 on OpenAlexaff
Steven D. Stovitz, Evert Verhagen, Ian Shrier

Bibliographic record

VenueBritish Journal of Sports Medicine · 2017
Typeeditorial
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsCausationObservational studyCausality (physics)ConfoundingCausal inferenceAssociation (psychology)Causal modelPsychologyOutcome (game theory)MedicineClinical psychologyPsychotherapistEpistemology

Abstract

fetched live from OpenAlex

Figure 1 The four general ways that variables A and B can be associated.Association simply means that knowing the value of one variable provides information on the other variable.Diagrams I and II are causal relationships.Diagrams III and IV are non-causal relationships.Protected by copyright, including for uses related to text and data mining, AI training, and similar technologies..

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)0.083
metaresearch head score (Gemma)0.378
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.917
Threshold uncertainty score0.440

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0830.378
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0090.004
Bibliometrics0.0070.005
Science and technology studies0.0040.009
Scholarly communication0.0130.014
Open science0.0060.003
Research integrity0.0240.027
Insufficient payload (model declined to judge)0.0070.004

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 head0.418
GPT teacher head0.523
Teacher spread0.105 · 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.

Study designNot applicable
DomainMethods
GenreEditorial

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
Published2017
Admission routes1
Has abstractyes

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