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Record W2946871537 · doi:10.1111/scd.12398

Association between epilepsy and oral maxillofacial trauma: A systematic review and meta‐analysis

2019· review· en· W2946871537 on OpenAlexaboutno aff
Saulo Gabriel Moreira Falci, Lucas Duarte‐Rodrigues, Ednele Fabyene Primo‐Miranda, Patrícia Furtado Gonçalves, Endi Lanza Galvão

Bibliographic record

VenueSpecial Care in Dentistry · 2019
Typereview
Languageen
FieldMedicine
TopicPharmacological Effects and Toxicity Studies
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMedicinePsychogenic diseaseMeta-analysisCochrane LibraryEpilepsyOral and maxillofacial surgeryMEDLINEDentistryInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

A systematic literature search was conducted (through April 2017), using Web of Science, PubMed and Virtual Health Library, manual reference list, and grey literature searches. The quality of the studies was evaluated using the Newcastle-Ottawa Quality Assessment Scale. The meta-analysis was performed using R software. A total of 30 studies was included in this review. From a total of 25 studies included in the meta-analysis, the prevalence of oral and maxillofacial injuries among epileptic subjects was 19%. Among the epileptic patients who suffered some type of injury due to epileptic seizures, 52% had facial soft tissue injuries (95%CI: 28-75%), 18% suffered dental trauma (95%CI: 11-29%), and 12% (95%CI: 4-28%) suffered maxillofacial fractures. Epileptic patients were more likely to have oral and maxillofacial injuries than healthy individuals (OR: 5.22, 95%CI: 2.84-9.36) and subjects with psychogenic nonepileptic seizures (OR: 2.77, 95%CI: 1.28-5.99), but not than patients with special needs (OR: 2.45,95%CI: 0.95-6.31).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.866
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0100.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.093
GPT teacher head0.405
Teacher spread0.312 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations19
Published2019
Admission routes1
Has abstractyes

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