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Record W4221088174 · doi:10.3389/fsurg.2022.863997

The Minimally Invasive Star-Shaped Incision Technique and the Linear Incision Technique With Tissue Preservation for Percutaneous Bone Conduction Devices: A Retrospective Cohort Study

2022· article· en· W4221088174 on OpenAlexaff
Ruben M. Strijbos, Samer Salameh, Aren Bezdjian, Sam J. Daniel, Hans G. X. M. Thomeer

Bibliographic record

VenueFrontiers in Surgery · 2022
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsMontreal Children's HospitalMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineSoft tissueTolerabilitySurgeryRetrospective cohort studyPercutaneousImplantCohortCohort studyAdverse effectInternal medicine

Abstract

fetched live from OpenAlex

Purpose: To compare intra- and postoperative outcomes between the standard linear incision technique with tissue preservation (LITT-P) and the minimally invasive star-shaped incision (SSI). Study Design: A retrospective cohort study. Methods: Primary outcomes evaluated operative time, implant survival, and intra-operative complications. A secondary outcome evaluated soft tissue tolerability assessed by the Holger's classification. Results: = 0.0001). No intra-operative complications were reported for both surgical approaches. Five implants were lost during postoperative follow-up: one in the LITT-P and four in the SSI cohort. Both cohorts showed favorable soft tissue tolerability. Less Holgers 1 and 2 and more Holgers 3 soft tissue reactions were observed after the LITT-P compared to the SSI. Conclusion: The novel SSI approach could be an alternative option based on the theoretical benefits and found favorable (and similar) soft tissue outcomes. Implant loss and surgical time are aspects to investigate regarding long-term durability and warrant further research.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.118
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.273
Teacher spread0.255 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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