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Performance of six bone collectors designed for dental implant surgery

2006· article· en· W2084021178 on OpenAlexaff
Vesa T. Kainulainen, Tiina Kainulainen, Kyösti Oikarinen, Robert P. Carmichael, George K.B. Sándor

Bibliographic record

VenueClinical Oral Implants Research · 2006
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDentistryMandible (arthropod mouthpart)ImplantBiomedical engineeringSignificant differenceDental implantTrap (plumbing)Materials scienceMedicineSurgeryBiologyEngineering

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of this study was to perform an in vitro comparison of six bone collectors for harvesting of particulate bone. MATERIAL AND METHODS: Four commercially available bone collectors (Frios, Osseous Coagulum Trap, ACE Autografter, Bone Trap) and two custom-designed models were tested. Three different in vitro tests were performed to determine the harvesting capabilities of the collectors. In test I, a bovine mandible was drilled and the bone collectors were used to collect bone chips. The harvested bone volumes and dry weights were measured after harvesting. In test II, three dental implant sites were prepared in a bovine mandible. The bones from the implant osteotomies were collected, and bone volumes and dry weights were measured. In test III, 1 ml of bone chips was mixed with water, and suctioned through the bone collectors. The volumes of the bone chips retained were measured to determine the efficiency of each collector. RESULTS: The Osseous Coagulum Trap and the custom-made collectors were the most effective instruments in test I. The mean volumes ranged from 0.17 to 0.38 ml. In test II, the difference between the collectors was small and the bone volume ranged from 0.28 to 0.37 ml. In test III, the Bone Trap became blocked before the other collectors, and its bone procurement was therefore limited. CONCLUSION: Comparison of six different bone collectors in this in vitro study showed that all collectors are usable in clinical situations but their effectiveness varies.

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 score0.372
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.251
GPT teacher head0.494
Teacher spread0.243 · 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

Citations26
Published2006
Admission routes1
Has abstractyes

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