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2020· article· en· W4206214771 on OpenAlexaboutno aff
Xiangdong Wu, Timothy Bhattacharyya, Marian T. Hannan, Douglas P. Kiel

Bibliographic record

VenueJAMA Internal Medicine · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

In Reply In their Letter in response to our Original Investigation, 1 Fuller-Thompson and Munro provide an interesting insight that improvements in environmental factors, specifically the phase out of leaded gasoline, could have contributed to the reduction in hip fractures observed in the United States and Canada.The role of lead is biologically plausible and important for the future: lead paint continues to be a source of lead exposure in Massachusetts and the rest of the United States.Shao and colleagues bring up 2 areas of hip fracture research that require further study.Although the members of the Framingham cohorts were overwhelmingly suburban residents, hip fractures are known to occur at a higher incidence in urban areas for unknown reasons.The long-standing US trend toward urbanization thus would tend to increase fracture incidence.Fall prevention efforts were not tracked in the Framingham cohorts.

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)0.009
metaresearch head score (Gemma)0.119
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.097
Threshold uncertainty score0.324

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.119
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0040.003
Scholarly communication0.0050.003
Open science0.0040.003
Research integrity0.0130.015
Insufficient payload (model declined to judge)0.0970.139

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 head0.101
GPT teacher head0.458
Teacher spread0.357 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2020
Admission routes1
Has abstractyes

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