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Record W2343067633

Mineral trioxide aggregate produces superior outcomes in vital primary molar pulpotomy.

2010· article· en· W2343067633 on OpenAlexaff
Tracy Doyle, Michael J Casas, David J. Kenny, Peter Judd

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldDentistry
TopicEndodontics and Root Canal Treatments
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPulpotomyMolarMedicineDentistryMineral trioxide aggregateEugenolOrthodonticsChemistry
DOInot available

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to investigate the outcomes of vital primary molar pulpotomy when there is no direct contact between eugenol and the vital pulp. Four pulpotomy techniques were compared: (1) ferric sulfate (FS) pulpotomy; (2) eugenol-free FS pulpotomy; (3) mineral trioxide aggregate (MTA) pulpotomy; and (4) FS/MTA pulpotomy. METHODS: The pulpotomy technique assigned to each molar was determined by random selection. Two blinded, disinterested raters classified each molar into 1 of 3 radiographic outcomes: (1) N=normal molar without pathologic change; (2) Po=pathologic change present, follow-up recommended; (3) Px=pathologic change present, extract. RESULTS: A total of 92 patients with 227 pulpotomy-treated molars returned for at least 1 recall examination. Median follow-up for molars was 24 months (range=12-38 months). MTA molars demonstrated significantly fewer Px radiographic outcomes than FS molars (P=.002, chi-square test). Eugenol-free FS molars demonstrated significantly more Px radiographic outcomes than MTA (P<.001, chi-square test) or FS/MTA (P=.002, chi-square test) molars. Significantly lower survival was demonstrated for eugenol-free FS molars compared to MTA molars (P=.02, log-rank test) over 6 to 38 months. CONCLUSIONS: Outcomes for mineral trioxide aggregate pulpotomy were superior to ferric sulfate and eugenol-free ferric sulfate pulpotomy after a median follow-up of 2 years.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.709

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)0.0000.000

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 head0.014
GPT teacher head0.234
Teacher spread0.220 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations42
Published2010
Admission routes1
Has abstractyes

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