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Record W54203154

Dental pulp neurophysiology: part 2. Current diagnostic tests to assess pulp vitality.

2009· article· en· W54203154 on OpenAlexaff
Ashraf Abd‐Elmeguid, Donald C. Yu

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldDentistry
TopicDental Anxiety and Anesthesia Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPulp (tooth)MedicineBlood supplyDentistryVitalityPulpitisRoot canalBlood flowSurgeryInternal medicineBiology
DOInot available

Abstract

fetched live from OpenAlex

In this second part of our 2-part review, we discuss recent research about pulp tests that determine the vitality of the tooth and clinically accepted pulp testers. A pain response to hot, cold or an electric pulp tester indicates the vitality of only a tooth's pulpal sensory supply; the response does not give any idea about the state of the pulp. Although the sensitivity of these tests is high, when false-positive and false-negative results occur, they may affect the treatment of the tooth. A tooth falsely diagnosed as nonvital with an electric pulp tester may undergo an unnecessary root canal, whereas one falsely diagnosed as vital may be left untreated, causing the necrotic tissue to destroy the supporting tissues (resorption). The vascular supply is more important to the determination of the health of the pulp than the sensory supply. Pulp death is caused by cessation of blood flow and may result in a necrotic pulp, even though the pulpal sensory supply may still be viable. The pulp can be healed only if the circulating blood flow is healthy. Although still under investigation, diagnostic devices that examine pulpal blood flow, such as the pulse oximeter and laser Doppler flowmetry, show promising results for the assessment of pulp vitality.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.001

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 head0.043
GPT teacher head0.288
Teacher spread0.245 · 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 teacher head, not a consensus.

Study designObservational
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

Citations47
Published2009
Admission routes1
Has abstractyes

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