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Record W1983663416 · doi:10.1016/s0022-3913(00)70095-3

Implant position record and implant position cast: Minimizing errors, procedures and patient visits in the fabrication of the milled-bar prosthesis

2000· article· en· W1983663416 on OpenAlexaff
Kenneth S. Hebel, Daniel F. Galindo, Reena C. Gajjar

Bibliographic record

VenueJournal of Prosthetic Dentistry · 2000
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsWestern University
Fundersnot available
KeywordsProsthesisBar (unit)ImplantFabricationMaterials sciencePosition (finance)Acrylic resinDentistryOrthodonticsBiomedical engineeringComputer scienceSurgeryComposite materialMedicineGeology

Abstract

fetched live from OpenAlex

This article describes a new rationale and method involved in the fabrication of a patient detachable prosthesis supported by a milled bar. This simple procedure improves prosthesis retention. The overdenture is processed directly over a milled bar, which provides an intimate relation between the bar and the acrylic resin denture base to create resistance against rotational and lateral forces acting on the prosthesis. Incorporating simple and predictable attachments, with low maintenance needs, controls resistance to dislodgment along the path of insertion of the prosthesis. The concepts used in the fabrication of the milled bar include an implant position record (IPR) and an implant position cast (IPC) to reduce the need for time-consuming procedures, such as sectioning the cast bar and soldering it to make it fit the abutments. This procedure also reduces the number of patient visits required of the completion of the prosthesis.

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)0.004
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.262
Teacher spread0.252 · 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

Citations32
Published2000
Admission routes1
Has abstractyes

Explore more

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