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Record W2885486887 · doi:10.1097/sla.0000000000003000

The Impact of Corporate Payments on Robotic Surgery Research

2018· article· en· W2885486887 on OpenAlexaboutno aff
Cory N. Criss, Mark MacEachern, Justin B. Dimick, Melinda Maggard‐Gibbons, Samir K. Gadepalli

Bibliographic record

VenueAnnals of Surgery · 2018
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineObservational studyReceiver operating characteristicMEDLINEPaymentRandomized controlled trialSubspecialtyReceiptReimbursementSurgeryFinanceFamily medicineAccountingInternal medicineHealth care

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaResearch integrity
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptMetaresearch
Domain: Incentives · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.565
metaresearch head score (Gemma)0.847
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.435
Threshold uncertainty score0.536

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5650.847
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0120.018
Science and technology studies0.0020.006
Scholarly communication0.0100.009
Open science0.0030.007
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.948
GPT teacher head0.685
Teacher spread0.263 · 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

Labeled directly by 2 models reading the full record.

Research integrityMetaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational
DomainIncentives
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

Citations35
Published2018
Admission routes1
Has abstractyes

Explore more

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