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Record W4407943198 · doi:10.1007/s11282-025-00808-3

Assessment of mastoid emissary foramen morphology: a multidetector computed tomography study

2025· article· en· W4407943198 on OpenAlexaff
Ahmet Faruk Ertürk, Gürkan Ünsal, Sevde Göksel, Elif Çelebi, Hamit Tunç, Maria Maddalena Marrapodi, Marco Cicciù, Giuseppe Minervini

Bibliographic record

VenueOral Radiology · 2025
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsWestern University
FundersUniversità degli Studi della Campania Luigi Vanvitelli
KeywordsMedicineForamenMultidetector computed tomographyComputed tomographyTemporal boneNuclear medicineHigh resolutionAnatomyRadiology

Abstract

fetched live from OpenAlex

OBJECTIVES: This study aimed to assess the occurrence and morphological features of the mastoid emissary foramen (MEF) using multidetector computed tomography (MDCT) images. The analysis highlights the clinical significance of these structures and their implications for surgical procedures. METHODS: A total of 357 patients were evaluated using MDCT in bone window mode with a high-resolution technique (1 mm). The presence, number, and mean diameter of the MEFs were recorded. Statistical analyses compared data between both sides and sexes. RESULTS: 714 sides from 357 patients (177 male, 180 female) were analyzed. The patients' ages ranged from 7 to 83 years, with a mean age of 25.6. MEFs were found in 329 patients, representing 92.15% of the total. The diameters of the MEFs ranged from 0.6 mm to 5.0 mm on the right side (mean 1.80 mm) and from 0.6 mm to 4.4 mm on the left side (mean 1.96 mm). Up to 3 MEFs were identified on the right side, and a maximum of 6 on the left. No significant differences in MEF presence were observed between sexes or between the left and right sides (p > 0.05). CONCLUSION: This study reveals a high prevalence and notable anatomical variations in the MEF, with MEFs larger than previously reported. At least one MEF was detected in 92.15% of cases, emphasizing the importance of comprehensive preoperative evaluation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.016
GPT teacher head0.323
Teacher spread0.307 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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