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Record W3139124630 · doi:10.52152/spr.2020.01.107

Multiple Odontogenic Keratocysts: A Case Report

2020· article· en· W3139124630 on OpenAlexaff
Harsh Kapil, Shaifi Jindal

Bibliographic record

VenueScience Progress and Research · 2020
Typearticle
Languageen
FieldMedicine
TopicOsteomyelitis and Bone Disorders Research
Canadian institutionsBrampton Civic Hospital
Fundersnot available
KeywordsMedicineAsymptomaticRadiographyMolarLesionDentistryDermatologyRadiologySurgery

Abstract

fetched live from OpenAlex

Multiple odontogenic keratocysts are a rare, but well recognized condition that characteristically affects the jaws of middle aged women .The radiographic appearance can vary from areas of radiolucency to mixed lesions and to opaque masses which are often bilaterally. This condition has been classified as sclerosing osteitis, multiple enostoses, diffuse chronic osteomyelitis and gigantiform cementoma. The lesion is usually benign and requires no treatment unless cosmetically concerning or becomes symptomatic. For the asymptomatic patient the best management consists of regular recall examination with prophylaxis and maintenance of good oral hygiene According to the recent review of literature, only five patients of florid cemento-osseous dysplasia from India have been reported (less than 2%). Here present such a rare case occurring in a 38 year –old- female who came with dull pain in upper right back teeth region since 6 months. Radiographically, it showed mixed radiolucent / radio-opaque mass in both right and left premolar to molar region.

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)0.003
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 score0.008
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0040.003
Scholarly communication0.0030.004
Open science0.0020.004
Research integrity0.0080.005
Insufficient payload (model declined to judge)0.0040.002

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 head0.129
GPT teacher head0.441
Teacher spread0.313 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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