MétaCan
Menu
Back to cohort
Record W2170498034 · doi:10.2176/nmc.ra.2015-0099

Craniopharyngioma in Children: Long-term Outcomes

2015· review· en· W2170498034 on OpenAlexaff
Paul Steinbok

Bibliographic record

VenueNeurologia medico-chirurgica · 2015
Typereview
Languageen
FieldMedicine
TopicPituitary Gland Disorders and Treatments
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsCraniopharyngiomaPsychosocialMedicineNeurocognitivePediatricsNeurosurgeryDiseaseIntensive care medicinePsychiatryCognitionSurgeryInternal medicine

Abstract

fetched live from OpenAlex

The survival rate for childhood craniopharyngioma has been improving, with more long-term survivors. Unfortunately it is rare for the patient to be normal, either from the disease itself or from the effects of treatment. Long-term survivors of childhood craniopharyngioma suffer a number of impairments, which include visual loss, endocrinopathy, hypothalamic dysfunction, cerebrovascular problems, neurologic and neurocognitive dysfunction. Pituitary insufficiency is present in almost 100%. Visual and hypothalamic dysfunction is common. There is a high risk of metabolic syndrome and increased risk of cerebrovascular disease, including stroke and Moyamoya syndrome. Cognitive, psychosocial, and emotional problems are prevalent. Finally, there is a higher risk of premature death among survivors of craniopharyngioma, and often this is not from tumor recurrence. It is important to consider craniopharyngioma as a chronic disease. There is no perfect treatment. The treatment has to be tailored to the individual patient to minimize dysfunction caused by tumor and treatments. So "cure" of the tumor does not mean a normal patient. The management of the patient and family needs multidisciplinary evaluation and should involve ophthalmology, endocrinology, neurosurgery, oncology, and psychology. Furthermore, it is also important to address emotional issues and social integration.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.671
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.056
GPT teacher head0.357
Teacher spread0.301 · 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 designOther design
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

Citations43
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueNeurologia medico-chirurgicaSame topicPituitary Gland Disorders and TreatmentsFrench-language works237,207