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Record W4414351955 · doi:10.1186/s40510-025-00579-x

Correction to: Skeletal versus conventional anchorage in dentofacial orthopedics: an international modified Delphi consensus study

2025· article· en· W4414351955 on OpenAlexaff
Lorenzo Franchi, Maria Denisa Statie, Tommaso Clauser, Marco Migliorati, Alessandro Ugolini, Rosaria Bucci, Roberto Rongo, Riccardo Nucera, Marco Portelli, James A. McNamara, Michele Nieri, Sercan Akyalçın, Fernanda Angelieri, Daniele Cantarella, Paolo Cattaneo, Lucia Cevidanes, Luca Contardo, Marie A. Cornelis, Renzo De Gabriele, Carlos Flores‐Mir, Daniela Gamba Garib, Giorgio Iodice, Antonino Lo Giudice, Luca Lombardo, Björn Ludwig, Cesare Luzi, Maria Costanza Meazzini, Peter Ngan, Tung Nguyen, Alexandra Papadopoulou, Spyridon N. Papageorgiou, Jae Hyun Park, Sabine Ruf, Bernardo Quiroga Souki, Benedict Wilmes, Heinz Winsauer

Bibliographic record

VenueProgress in Orthodontics · 2025
Typearticle
Languageen
FieldHealth Professions
TopicDental Education, Practice, Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDelphi methodDentofacial DeformityDelphiMEDLINEMalocclusion

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.015
metaresearch head score (Gemma)0.107
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.107
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0180.005

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 head0.120
GPT teacher head0.544
Teacher spread0.424 · 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
GenreOther

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
Published2025
Admission routes1
Has abstractno

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