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Record W2914358869 · doi:10.3171/2018.10.peds18328

Prospective multicenter studies in pediatric hydrocephalus

2019· review· en· W2914358869 on OpenAlexaff
John R. W. Kestle, Jay Riva-Cambrin

Bibliographic record

VenueJournal of Neurosurgery Pediatrics · 2019
Typereview
Languageen
FieldNeuroscience
TopicCerebrospinal fluid and hydrocephalus
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineHydrocephalusProspective cohort studyPediatricsClinical trialIntraventricular hemorrhageMulticenter trialMulticenter studyCohort studyIntensive care medicineRandomized controlled trialSurgeryInternal medicineGestational agePregnancy

Abstract

fetched live from OpenAlex

Prospective multicenter clinical research studies in pediatric hydrocephalus are relatively rare. They cover a broad spectrum of hydrocephalus topics, including management of intraventricular hemorrhage in premature infants, shunt techniques and equipment, shunt outcomes, endoscopic treatment of hydrocephalus, and prevention and treatment of infection. The research methodologies include randomized trials, cohort studies, and registry-based studies. This review describes prospective multicenter studies in pediatric hydrocephalus since 1990. Many studies have included all forms of hydrocephalus and used device or procedure failure as the primary outcome. Although such studies have yielded useful findings, they might miss important treatment effects in specific subgroups. As multicenter study networks grow, larger patient numbers will allow studies with more focused entry criteria based on known and evolving prognostic factors. In addition, increased use of patient-centered outcomes such as neurodevelopmental assessment and quality of life should be measured and emphasized in study results. Well-planned multicenter clinical studies can significantly affect the care of children with hydrocephalus and will continue to have an important role in improving care for these children and their families.

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)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.137
GPT teacher head0.376
Teacher spread0.239 · 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 designNot applicable
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

Citations20
Published2019
Admission routes1
Has abstractyes

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