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Record W4324124170 · doi:10.59124/guhes.1155747

Rehabilitation of Surgically Reconstructed Partially Edentulous Mandible with Iliac Crest Graft After Ameloblastoma Resection with an Implant-Supported “Toronto Prosthesis”

2023· article· en· W4324124170 on OpenAlexaboutno aff
Esra Kaynak Öztürk, Şule KAHRAMAN, Ertan Delilbaşı, Emre BARIŞ, Merve Bankoğlu Güngör

Bibliographic record

VenueJournal of Gazi University Health Sciences Institute · 2023
Typearticle
Languageen
FieldDentistry
TopicOral and Maxillofacial Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsIliac crestMedicineAmeloblastomaProsthesisMandible (arthropod mouthpart)ImplantIliac boneRehabilitationDentistryMandibular NeoplasmsSurgeryMaxilla

Abstract

fetched live from OpenAlex

Ameloblastomas are locally invasive and benign odontogenic tumors with a high long-term recurrence rate. These lesions can cause serious anomalies in the facial area and alveolar bones, leading deformity and deterioration of functions. Wide local excision and reconstruction are required for the surgical treatment of these tumors. Aggressive resection effectively eliminates tumors; however, this approach may cause various problems that need reconstruction to restore the oral functions. In the present case report, the rehabilitation of surgically reconstructed partially edentulous mandible with iliac crest graft after ameloblastoma resection with an implant-supported “Toronto Prosthesis” is presented. In the surgical procedure, mandible was partially resected and simultaneous iliac bone graft was applied. Then, four dental implants were inserted into the reconstructed bone and implant-supported Toronto prosthesis was fabricated. The patient was satisfied with the final result of the treatment and recurrence was not observed during the 2-year clinical follow-up.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.283
Teacher spread0.265 · 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 designCase report
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Gazi University Health Sciences InstituteSame topicOral and Maxillofacial PathologyFrench-language works237,207