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Record W4237045047 · doi:10.14740/jmc3325

Report of Seven Cases of Langerhans Cell Histiocytosis in a Single Hospital in Japan

2019· article· en· W4237045047 on OpenAlexvenueno aff
Takuji Tanaka, Riyoko Niwa, Fumimasa Etori, Asuka Ohashi, Ryogo Aoki, Naomi Kawaguchi, Toshimasa Sakakima, Rina Miyamae, Masashi Matsuyama, Naoki Watanabe

Bibliographic record

VenueJournal of Medical Cases · 2019
Typearticle
Languageen
FieldMedicine
TopicHistiocytic Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLangerhans cell histiocytosisPathologicalHistiocyteHistiocytosisPresentation (obstetrics)DiseasePediatricsPathologyRadiology

Abstract

fetched live from OpenAlex

Langerhans cell histiocytosis (LCH), which shows a variable clinical presentation, is a disease that involves the proliferation of histiocytes. LCH occurs at any age and can affect a single system or multiple systems. The successful and accurate diagnosis of cases involving multiple organs requires a multidisciplinary approach. Since LCH receives limited attention, failure to recognize this disease in the early pediatric age can result in progression into adulthood. This study presents the clinical manifestations, imaging findings, pathological and cytological features, treatment strategies and prognoses of seven cases of LCH that are managed in a single hospital over the last 12 years. The ages of the patients ranged from 1 to 58 years. The sites at which LCH developed included the skin, lung, bone, lymph nodes and brain: one patient (adult male) had single-system/single-site LCH, three patients (adult male, adult female and girl) had single-system/multi-site LCH and three patients (adult female and two girls) had multi-system/multi-site LCH. BRAF V600E mutation was suggested in an adult female with multi-system/multi-site LCH and an adult male with single-system/multi-site LCH. We also discuss the pathogenesis and review the relevant literature. J Med Cases. 2019;10(7):209-217 doi: https://doi.org/10.14740/jmc3325

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)0.001
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 score0.182
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.018
GPT teacher head0.282
Teacher spread0.264 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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