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Record W2784775832 · doi:10.1097/prs.0000000000004065

Discussion: Conceptual Considerations for Payment Bundling in Breast Reconstruction

2018· letter· en· W2784775832 on OpenAlexaff
Arjun Kanuri, David Song

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2018
Typeletter
Languageen
FieldMedicine
TopicBreast Implant and Reconstruction
Canadian institutionsCegep de Sept Iles
Fundersnot available
KeywordsPaymentUniversity hospitalPlastic surgeryRelation (database)MedicineGeneral surgeryLibrary scienceFamily medicineSurgeryBusinessFinanceComputer science

Abstract

fetched live from OpenAlex

Washington, D.C. From the Department of Plastic Surgery, MedStar Georgetown University Hospital. Received for publication September 19, 2017; accepted September 22, 2017. Disclosure: The authors have no financial interest to declare in relation to the content of this Discussion or of the associated article. David H. Song, M.D., M.B.A., Department of Plastic Surgery, MedStar Georgetown University Hospital, 3800 Reservoir Road, Washington, D.C. 20007, [email protected]

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)0.045
metaresearch head score (Gemma)0.079
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.046
Threshold uncertainty score0.239

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.079
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0090.020
Scholarly communication0.0120.017
Open science0.0060.006
Research integrity0.0460.041
Insufficient payload (model declined to judge)0.0180.002

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 head0.036
GPT teacher head0.256
Teacher spread0.220 · 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 designNot applicable
Domainnot available
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

Citations3
Published2018
Admission routes1
Has abstractyes

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