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Record W2995145907

Dentin Hypersensitivity: Etiology, Diagnosis, and Management.

2019· article· en· W2995145907 on OpenAlexaff
Justin Felix, Aviv Ouanounou

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldDentistry
TopicDental Erosion and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDentin hypersensitivityEtiologyMedicineDentinDentistryPresentation (obstetrics)Case presentationDental practiceSurgeryPathology
DOInot available

Abstract

fetched live from OpenAlex

Dentin hypersensitivity, a commonly presenting condition, is described as sharp pain resulting from the exposure of open dentin tubules to the oral environment in response to a varied assortment of stimuli. It is sometimes a difficult condition to diagnose, because the diagnosis is one of exclusion and all other potential causes must be examined first. The heterogeneity of this presentation, ranging from a minor inconvenience to the patient to a near incapacitating quality-of-life disorder, along with the wide array of treatment strategies pose challenges to the clinician. All dental professionals should be familiar with dentin hypersensitivity, such that they are able to effectively relieve patients of their pain. This article discusses the etiology, diagnosis, and effective management of dentin hypersensitivity.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
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 head0.019
GPT teacher head0.230
Teacher spread0.211 · 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
GenreReview

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

Citations23
Published2019
Admission routes1
Has abstractyes

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