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Record W2921252260 · doi:10.1177/2513826x19831719

Bilateral Facial Weakness in a Syndromic Patient: Cadaveric Fascia Lata Graft for Lower Lip Deformity Correction

2019· article· en· W2921252260 on OpenAlexvenueno aff
Francesco Silan, Fabio Consiglio, Francesco Dell’Antonia, Alessandra Vidali, Christian Rizzetto, Giorgio Berna

Bibliographic record

VenuePlastic Surgery Case Studies · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCraniofacial Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFascia lataSurgeryDroolingFacial weaknessWeaknessMicrostomia

Abstract

fetched live from OpenAlex

Fazio-Londe syndrome is a rare neurodegenerative disorder caused by riboflavin transporter deficiency. Clinical presentation includes variable hypotonia or muscle weakness, sensory gait ataxia, optic atrophy, sensorineural hearing loss, and bulbar palsies. We described the case of a 37-year-old patient with a suspected Fazio-Londe syndrome who was referred to us for oral incompetence with dysarthria and drooling and impaired facial expression. First, we provided static sling suspension of the lower lip with Silhouette sutures (Silhouette Lift). Due to recurrent sutures exposure, we had to remove the sutures and insert a bilateral fascia lata graft to resuspend the lower lip. Two months postoperatively, the patient had good oral competence and there were no complications. In our opinion, this is a simple and minimally invasive procedure that can restore the right lower lip position in challenging patients.

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)0.001
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.251
Teacher spread0.238 · 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
Published2019
Admission routes1
Has abstractyes

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