MétaCan
Menu
Back to cohort

Toward Enhancing the Rigor of Causal-Inference Studies

2019· letter· en· W2913529748 on OpenAlexaff
Igor Karp

Bibliographic record

VenueAnnals of the American Thoracic Society · 2019
Typeletter
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of British ColumbiaUniversity of TorontoSt. Paul's HospitalUniversité de MontréalSt. Michael's HospitalWestern University
Fundersnot available
KeywordsMedicineCausal inferenceInferenceMEDLINEIntensive care medicineEpistemologyPathology

Abstract

fetched live from OpenAlex

individuals diagnosed at an older age must live to that age to be diagnosed and included in the cystic fibrosis registry, which leads to an "immortal" survival time bias.The relationship between age at diagnosis and survival is complex and must be interpreted with caution, as noted by Fieuws and colleagues.Although older age at diagnosis inherently increases the risk for death because of age effects, older age at diagnosis may also reflect milder disease and reduced risk for death.However, being diagnosed later in life also means there were additional years of untreated cystic fibrosis, which could result in a negative effect on health and, hence, result in an increased risk for death.We again thank Fieuws and colleagues for bringing attention to this important issue and for highlighting that older age at diagnosis may not necessarily be a risk factor for worse outcomes in adult-diagnosed cystic fibrosis, a message we would not want miscommunicated to patients.

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)0.719
metaresearch head score (Gemma)0.932
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.281
Threshold uncertainty score0.347

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7190.932
Meta-epidemiology (narrow)0.0030.005
Meta-epidemiology (broad)0.0110.011
Bibliometrics0.0120.007
Science and technology studies0.0030.022
Scholarly communication0.0170.032
Open science0.0140.013
Research integrity0.0290.042
Insufficient payload (model declined to judge)0.0110.006

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 head0.850
GPT teacher head0.611
Teacher spread0.238 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreCommentary

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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of the American Thoracic SocietySame topicMeta-analysis and systematic reviewsFrench-language works237,207