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Record W3158342289 · doi:10.33915/etd.5107

Assessment of Surgical Guide Accuracy Utilizing a Digital Workflow

2018· dissertation· en· W3158342289 on OpenAlexaboutno aff
Nicole Irene Andreini

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsImplantWorkflowDICOMOrthodonticsSuperimpositionDentistryComputer scienceMedicineSurgeryComputer visionArtificial intelligenceDatabase

Abstract

fetched live from OpenAlex

Objectives: To determine the accuracy of placing implants using a digital workflow. Variables were compared to see if there was an effect on implant position accuracy. These included bone level (BL) versus bone level tapered (BLT) implants, positioning of implants adjacent to tooth support versus further across an edentulous ridge, and impact of the surgical guide requiring adjustment to fully seat. Furthermore, two methods of post-operative assessment were compared to evaluate consistency of each approach.;Methods: A typical work up for digital implant planning was performed on a sectioned pig jaw. The DICOM CBCT file and IOS STL file were imported into coDiagnostiX to virtually plan implant placement (Dentalwings, Montreal, Canada). Two implants were planned for each of ten specimens, with one positioned adjacent to the Surgical guide tooth support (Implant A) and a second positioned more distally along the edentulous ridge (Implant B). The guide was designed and 3-D printed with 5 mm sleeves to allow full preparation of the osteotomy. The sites were prepared following Straumann guided surgery protocols, with the exception of utilizing irrigation. The implant was free-handed into position, until full depth of placement was achieved. The jaws were then post-operatively assessed using two methods: a post-operative CBCT scan and an intraoral scan of implant scan bodies. Each set of files were overlapped with the treatment evaluation tool of the coDiagnostiX software, to compare the planned to the resulting implant position.;Results: The average error was more pronounced at both the base and tip of implants in the mesial direction. An average 3D offset of 1.43 mm was observed at the coronal aspect, with a little higher average offset of 2.04 mm at the implant apex. Also, the average angular deviation was 5.17°. There was no significant difference found between the post-operative methods of assessment. Significant differences were shown between implant A and B when comparing the depth of placement. Differences were also found between BL and BLT implant types in angular deviation and in the mesial/distal direction at the platform of the implant. When the guide required adjustments, a significant difference in positioning was found in the buccal/lingual direction.;Conclusions: Post-operative assessment using a CBCT or IOS of scan bodies are comparable methods to evaluate planned versus placed implant positioning. Flexure of the surgical guide may have caused implants placed further from the tooth support to be positioned deeper than planned. BLT implants showed better angular accuracy than

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)0.006
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.393
Teacher spread0.364 · 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 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

Citations3
Published2018
Admission routes1
Has abstractyes

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