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Record W2198240143 · doi:10.1017/cbo9780511545252.013

The treatment of Langerhans cell histiocytosis

2005· book-chapter· en· W2198240143 on OpenAlexaff
Helmut Gadner, Stephan Ladisch

Bibliographic record

VenueCambridge University Press eBooks · 2005
Typebook-chapter
Languageen
FieldMedicine
TopicHistiocytic Disorders and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLangerhans cell histiocytosisEtiologyHistiocytosisPathogenesisMedicineDiseaseRadiation therapyDermatologyImmunologyPathologySurgery

Abstract

fetched live from OpenAlex

INTRODUCTION Background The treatment of Langerhans cell histiocytosis (LCH) has varied greatly over the past century, and is still controversial. Early treatment approaches reflected contemporary views on disease pathogenesis, which included granulomatous, inflammatory or infectious origins for LCH. Consequently, children with LCH were treated with antibiotics, anti-inflammatory agents including steroids, and with radiation therapy and over the last 30–40 years with cytotoxic chemotherapy. While varying degrees of success have been reported, it remains true that only once the issues of aetiology and pathogenesis have been resolved can a definitive therapy be envisioned. Nevertheless, systematic approaches to diagnosis and treatment, which were major advances of the 1980s, have improved the outlook for children with LCH and are the main focus of this chapter. Historical perspective The first systematic treatment trial of children with LCH was that of Lahey (1962). In that study, children were matched for age and extent of disease, and outcome was analysed according to whether or not ‘specific’ treatment for LCH was given. Treatment was not controlled however, and a variety of agents, including antibiotics, steroids and cytotoxic drugs were given, making direct comparisons problematic. Nevertheless, the principal, and important, finding of this study was a significant increase in survival in the group of children receiving therapy compared to those who were untreated. These findings led to a number of studies in which, unfortunately, the patient populations varied greatly with respect to extent of disease. This variability made interpretation of results difficult, and conclusions regarding superior treatment approaches were at best tenuous.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.004

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 head0.020
GPT teacher head0.206
Teacher spread0.186 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations14
Published2005
Admission routes1
Has abstractyes

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