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Record W4205999487 · doi:10.1038/s41433-021-01897-0

Cohort studies investigating the effects of exposures: key principles that impact the credibility of the results

2022· editorial· en· W4205999487 on OpenAlexaff
Anna Miroshnychenko, Dena Zeraatkar, Mark Phillips, Sophie J. Bakri, Lehana Thabane, Mohit Bhandari, Varun Chaudhary, Charles C. Wykoff, Sobha Sivaprasad, Peter K. Kaiser, David Sarraf, Sunir J. Garg, Rishi P. Singh, Frank G. Holz, Tien Yin Wong, Robyn H. Guymer

Bibliographic record

VenueEye · 2022
Typeeditorial
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster UniversityImpact
FundersNational Institute for Health and Care Research
KeywordsMedicineCredibilityKey (lock)Cohort studyCohortMEDLINEEnvironmental healthIntensive care medicineComputer scienceInternal medicineBiologyPolitical scienceComputer security

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 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)0.054
metaresearch head score (Gemma)0.156
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 score0.946
Threshold uncertainty score0.288

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.156
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0090.005
Bibliometrics0.0060.004
Science and technology studies0.0040.008
Scholarly communication0.0110.007
Open science0.0110.002
Research integrity0.0350.037
Insufficient payload (model declined to judge)0.0060.005

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 head0.031
GPT teacher head0.315
Teacher spread0.284 · 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

Citations15
Published2022
Admission routes1
Has abstractno

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