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Record W2335605979 · doi:10.1056/nejme1310554

When It Comes to Trials, Do We Get What We Pay For?

2013· letter· en· W2335605979 on OpenAlexaff
P.J. Devereaux, Salim Yusuf

Bibliographic record

VenueNew England Journal of Medicine · 2013
Typeletter
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsPopulation Health Research Institute
Fundersnot available
KeywordsMedicineClinical trialGovernment (linguistics)Psychological interventionAlternative medicineHealth careRandomized controlled trialPublic healthClinical PracticeFamily medicineNursingSurgeryPathologyEconomic growth

Abstract

fetched live from OpenAlex

Randomized clinical trials supported by government agencies are critical for advancing knowledge about a large number of interventions (e.g., generic therapies, health system strategies, and surgery) that are relevant to clinical practice and public health. There is a need, however, for timely publication of trial results, to allow health care providers to use the information promptly.In this issue of the Journal, Gordon et al.1 report a thought-provoking study of 244 trials funded by the National Heart, Lung, and Blood Institute. Although data collection for these trials was completed between January 1, 2000, and December 31, 2011, the results of . . .

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)0.234
metaresearch head score (Gemma)0.673
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.766
Threshold uncertainty score0.945

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2340.673
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0050.006
Science and technology studies0.0060.025
Scholarly communication0.0200.037
Open science0.0090.006
Research integrity0.0860.104
Insufficient payload (model declined to judge)0.0130.014

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 head0.444
GPT teacher head0.448
Teacher spread0.004 · 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 designNot applicable
DomainEvaluation
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

Citations13
Published2013
Admission routes1
Has abstractyes

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