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Record W4413050436 · doi:10.1177/08987564251363169

Comparison of 2 Techniques for Removal of Displaced Root Fragments From the Mandibular Canal in Canine Cadavers

2025· article· en· W4413050436 on OpenAlexaff
Michael Balke, Sandra Manfra Marretta, Glenna E. Mauldin

Bibliographic record

VenueJournal of Veterinary Dentistry · 2025
Typearticle
Languageen
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineRoot canalDentistryDisplacement (psychology)CadaverComplicationOrthodonticsMandibular canineImplantDental implantAnatomySurgery

Abstract

fetched live from OpenAlex

Tooth extraction is the most common oral surgical procedure performed in human and veterinary dentistry. One possible complication during extraction is root fragment displacement into adjacent anatomical spaces. Root fragment displacement into adjacent anatomical spaces can lead to serious side effects including pain and infection; therefore, displaced fragments should be removed when possible. Root fragment removal techniques from the mandibular canal have not been studied in veterinary or human dentistry. A reported complication of dental implant placement in humans is displacement into the mandibular canal, and techniques have been developed to remove these displaced implants. This report compares 2 techniques to remove displaced root fragments from the mandibular canal in canine cadavers based on previously published methods to remove displaced dental implants from the mandibular canal in humans.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.364
Teacher spread0.337 · 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 designBench or experimental
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
Published2025
Admission routes1
Has abstractyes

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