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Record W192376365 · doi:10.1177/229255030701500312

Post-traumatic pseudolipoma of the forehead

2007· article· en· W192376365 on OpenAlexaffvenue
David Horovitz, Damir B. Matic

Bibliographic record

VenueCanadian Journal of Plastic Surgery · 2007
Typearticle
Languageen
FieldMedicine
TopicTumors and Oncological Cases
Canadian institutionsWestern University
Fundersnot available
KeywordsForeheadMedicineHistopathologyForcepsDermoid cystFrontal boneLipomaDifferential diagnosisMagnetic resonance imagingSkullLesionSurgeryAnatomyRadiologyPathology

Abstract

fetched live from OpenAlex

A forehead lipoma is a rare finding in a child, and one that penetrates the underlying layers of muscle and bone to attach to dura has not previously been reported. Two such cases, both in children who underwent uneventful deliveries aided by forceps, are presented. Both lesions were present at birth and, based on clinical findings, were originally thought to be dermoid cysts. Dermoid cysts could not be ruled out with computed tomography and magnetic resonance imaging. Histopathology identified fibrofatty tissue consistent with lipoma. Both lesions extended from the subcutaneous tissue through the frontalis muscle and frontal bone to the dura. Given these findings and the history of forceps delivery, the most likely diagnosis is post-traumatic pseudolipoma. This lesion should be considered in the differential diagnosis of congenital lesions of the forehead, particularly if there is a history of forceps delivery or other trauma to the area.

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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.033
GPT teacher head0.258
Teacher spread0.225 · 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
Published2007
Admission routes2
Has abstractyes

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