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Record W1779567739 · doi:10.3171/2015.2.peds14612

Exploring predictors of surgery and comparing operative treatment approaches for pediatric intracranial arachnoid cysts: a case series of 83 patients

2015· article· en· W1779567739 on OpenAlexaff
Mohsin Ali, Michael Bennardo, Saleh A. Almenawer, Nirmeen Zagzoog, Alston A. Smith, Dyda Dao, BHSc, Olufemi Ajani, Forough Farrokhyar, Sheila K. Singh

Bibliographic record

VenueJournal of Neurosurgery Pediatrics · 2015
Typearticle
Languageen
FieldNeuroscience
TopicCerebrospinal fluid and hydrocephalus
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineArachnoid cystCystSurgeryHydrocephalusReceiver operating characteristicLogistic regressionCutoffRadiologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECT: Although intracranial arachnoid cysts are a common incidental finding on pediatric brain imaging, only a subset of patients require surgery for them. For the minority who undergo surgery, the comparative effectiveness of various surgical approaches is debated. The authors explored predictors of surgery and compared operative techniques for pediatric patients with an intracranial arachnoid cyst seen at a tertiary care center. METHODS: The authors reviewed records of pediatric patients with an intracranial arachnoid cyst. For each patient, data on baseline characteristics, the method of intervention, and surgical outcomes for the initial surgery were extracted, and cyst size at diagnosis was calculated (anteroposterior × craniocaudal × mediolateral). Baseline variables were analyzed as predictors of surgery by using logistic regression modeling, excluding patients whose surgery was not related to cyst size (i.e., those with obstructive hydrocephalus secondary to the cyst compressing a narrow CSF flow pathway or cyst rupture/hemorrhage). Data collected regarding surgical outcomes were analyzed descriptively. RESULTS: Among 83 pediatric patients with an intracranial arachnoid cyst seen over a 25-year period (1989-2013), 27 (33%) underwent surgery; all had at least 1 cyst-attributed symptom/finding. In the multivariate model, age at presentation and cyst size at diagnosis were independent predictors of surgery. Cyst size had greater predictive value; specifically, the area under the curve for the receiver-operating-characteristic curve was 0.89 (95% CI 0.82-0.97), with an ideal cutoff point of ≥ 68 cm(3). This cutoff point had 100% sensitivity (95% CI 79%-100%), 75% specificity (95% CI 61%-85%), a 53% positive predictive value (95% CI 36%-70%), and a 100% negative predictive value (95% CI 91%-100%); the positive likelihood ratio was 4.0 (95% CI 2.5-6.3), and the negative likelihood ratio was 0 (95% CI 0-0.3). Although the multivariate model excluded 7 patients who underwent surgery (based on prespecified criteria), excluding these 7 cases did not change the overall findings, as shown in a sensitivity analysis that included all the cases. Descriptive results regarding surgical outcomes did not indicate any salient differences among the surgical techniques (endoscopic fenestration, cystoperitoneal shunting, or craniotomy-based procedures) in terms of symptom resolution within 6 months, need for reoperation to date, cyst-size change from before the operation, morbidity, or mortality. CONCLUSIONS: The results of these exploratory analyses suggest that pediatric patients with an intracranial arachnoid cyst are more likely to undergo surgery if the cyst is large, compresses a narrow CSF flow pathway to cause hydrocephalus, or has ruptured/hemorrhaged. There were no salient differences among the 3 surgical techniques for several clinically important outcomes. A prospective multicenter study is required to enable more robust analyses, which could ultimately provide a decision-making framework for surgical indications and clarify any differences in the comparative effectiveness of surgical approaches to treating pediatric intracranial arachnoid cysts.

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)0.002
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 score0.344
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.237
GPT teacher head0.290
Teacher spread0.053 · 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

Citations68
Published2015
Admission routes1
Has abstractyes

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