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Record W4386254090 · doi:10.1111/odi.14722

Cysts of the jaws: A multicentre study

2023· article· en· W4386254090 on OpenAlexaff
Kittipong Dhanuthai, Soranun Chantarangsu, Poramaporn Klanrit, Nutchapon Chamusri, Pouyan Aminishakib, Neda Kardouni Khoozestani, Arina Morozan, Celina Tang, Riponjot Singh, Mark Darling

Bibliographic record

VenueOral Diseases · 2023
Typearticle
Languageen
FieldDentistry
TopicOral and Maxillofacial Pathology
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineKeratocystCystRadicular CystDentigerous cystMedical diagnosisOdontogenic cystDifferential diagnosisOral and maxillofacial pathologySurgeryDentistryRadiologyPathology

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine the relative frequency, demographic and pathologic profiles of patients diagnosed with cysts of the jaws. MATERIALS AND METHODS: Biopsy records of the participating institutions from 2000 to 2020 were reviewed for lesions diagnosed in the cyst category. Demographic data, the location of the cysts and pathologic diagnoses were collected. Data were analyzed by appropriate statistics using IBM SPSS software version 28.0. RESULTS: From 148,353 accessioned cases, 25,628 cases (17.28%) were diagnosed in the cyst category. Mean age of the patients ± SD = 42.62 ± 19.36 years. Paediatric patients (aged ≤ 16 years) accounted for 9.63%, while geriatric patients (aged ≥ 65) comprised 14.22% of all the patients. The male-to-female ratio was 1.27:1. The majority of the lesions were encountered in the mandible. The most prevalent cyst was radicular cyst followed by dentigerous cyst and odontogenic keratocyst. In the paediatric group, dentigerous cyst was the most prevalent, whereas in the geriatric group, radicular cyst was the most common. CONCLUSIONS: In general, the results of this study are in accordance with previous studies. This study provides an invaluable database for clinicians when formulating clinical differential diagnoses as well as for pathologists in rendering the final diagnosis.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.025
GPT teacher head0.305
Teacher spread0.280 · 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.

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

Citations8
Published2023
Admission routes1
Has abstractyes

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