Evidence map›Paper›PMID 41427130›Full record

ArticleFrontiers in veterinary science2025

Comparison of dental surface of canine teeth in dogs after crown preparation using CBCT-based surface reconstruction or conventional dental impression materials.

Benjamin Metje, Jan Schreyer, Matthias Lüpke, Sebastian Meller, Jan R S Klasen, Jerzy Pawel Gawor

Abstract read
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Article in Frontiers in veterinary science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Benjamin MetjeUniversity of Veterinary Medicine Hannover, Foundation, Hannover, Germany.
Jan SchreyerTierärztliche Gemeinschaftspraxis am Kaßberg, Dr. Plümer & Dr. Schreyer, Chemnitz, Germany.
Matthias LüpkeUniversity of Veterinary Medicine Hannover, Foundation, Hannover, Germany.
Sebastian MellerUniversity of Veterinary Medicine Hannover, Foundation, Hannover, Germany.
Jan R S KlasenTierklinik Germersheim GmbH, Germersheim, Germany.
Jerzy Pawel GaworUniversity of Veterinary Medicine Hannover, Foundation, Hannover, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This cadaver study evaluated accuracy of Cone Beam Computed Tomography (CBCT)-derived surface models in reproducing crown-prepared canine teeth and compared them with conventional impressions/plaster models as the current gold standard in veterinary prosthodontics. Methods: Forty canine teeth underwent crown preparation. Conventional impressions with plaster model fabrication and CBCT scans at two resolutions (n = 20 0.12 mm, n = 20 0.09 mm) were obtained. Models and original teeth were digitized by precise surface scanning. Surface distance from plaster models and CBCT datasets was compared with the original teeth (reference). Mean deviations were calculated and compared statistically. Results: Plaster models showed deviations of 9.3-56.4 μm. High-resolution CBCT scans yielded 21.3-36.4 μm; low-resolution scans 30.6-514.5 μm, with one outlier excluded. Within both cohorts, CBCT and plaster model deviations did not differ significantly; however, the two CBCT protocols differed significantly ( Conclusion: Both CBCT protocols produced deviations comparable to plaster models in this in-vitro single-tooth setup. Higher-resolution CBCT improved accuracy, but all CBCT datasets remained within clinically acceptable ranges. CBCT may represent a feasible alternative for digital impressions in veterinary prosthodontics, though radiation exposure and clinical applicability must be considered.

Indexed as

accuracyCBCTcone beam computed tomographydigital impressionprosthodonticsstone model

Identifiers

PMID41427130
PMCPMC12711511

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.