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Record W2765635956 · doi:10.1088/2057-1976/aa952b

Injectable glass polyalkenoate cements: evaluation of their rheological and mechanical properties with and without the incorporation of lidocaine hydrochloride

2017· article· en· W2765635956 on OpenAlexafffund
Pavithra Niranjan, Adel Alhalawani, Sunjeev Phull, I Beniluz, B. Radha Krishnan, Paul Zalzal, Mark R Towler

Bibliographic record

VenueBiomedical Physics & Engineering Express · 2017
Typearticle
Languageen
FieldDentistry
TopicEndodontics and Root Canal Treatments
Canadian institutionsSt. Michael's HospitalOakville-Trafalgar Memorial HospitalToronto Metropolitan University
FundersCanadian Institutes of Health ResearchInternational Business Machines Corporation
KeywordsLidocaine HydrochlorideHydrochlorideLidocaineFourier transform infrared spectroscopyMaterials scienceCementNuclear chemistryChemistryChemical engineeringChromatographyOrganic chemistryComposite materialAnesthesiaMedicine

Abstract

fetched live from OpenAlex

Lidocaine hydrochloride is used as an anesthetic in many clinical applications. This short communication investigates the effect of complete substitution of lidocaine hydrochloride for deionized (DI) water on the physico-chemical properties of two novel glass polyalkenoate cements. Substituting DI water with lidocaine hydrochloride resulted in cements with shorter working times but comparable setting times and mechanical properties. Fourier transform infrared spectroscopy confirmed that the setting reaction in cements containing DI water and lidocaine hydrochloride was completed within 24 h, post cement preparation and maturation. Further, it was explained that lidocaine hydrochloride binds to poly(acrylic) acid (PAA) due to electrostatic forces between the positively charged amino group of lidocaine hydrochloride and the carboxylic group of the PAA, resulting in a compact poly-complex precipitate.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.264
Teacher spread0.226 · 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

Citations6
Published2017
Admission routes2
Has abstractyes

Explore more

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