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Record W2598002665 · doi:10.4103/0972-4052.203194

Two-piece obturator using “lock-and-key” mechanism

2017· article· en· W2598002665 on OpenAlexaff
Prema Sukumaran, MichaelR Fenlon

Bibliographic record

VenueThe Journal of Indian Prosthodontic Society · 2017
Typearticle
Languageen
FieldEngineering
TopicMechanics and Biomechanics Studies
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsLock (firearm)Key (lock)Mechanism (biology)Computer scienceComputer securityEngineeringMechanical engineeringPhilosophy

Abstract

fetched live from OpenAlex

This paper describes a method used for the fabrication of a two-piece denture obturator for a patient who had surgical removal of the premaxilla due to squamous cell carcinoma. The patient had been wearing a two-piece obturator but encountered difficulty in inserting the prosthesis. In this case report, a lock-and-key mechanism was used to easily assemble the two-piece prosthesis intraorally. A keyhole was designed on the obturator to act as the lock while the denture was used as the key that fitted into the keyhole. This mechanism facilitated insertion and provided retention for the prosthesis. Heat-cured resilient acrylic material (Molloplast B®), which was used to fabricate the obturator, was a nonirritant, nontoxic, tissue-compatible material. It also did not contain plasticizers, therefore eliminating the problems associated with leaching out of plasticizers. The use of this flexible and resilient material allowed the obturator to engage in the undercuts without causing trauma and irritation to the soft tissues in the region of the defect. To conclude, the “lock-and-key” mechanism used in the fabrication of the two-piece denture obturator provided the patient with a lightweight, comfortable, and user-friendly form of prostheses.

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)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.001
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 head0.027
GPT teacher head0.263
Teacher spread0.236 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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