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Record W2047297625 · doi:10.3171/2011.12.focus11315

Inequities in access to pediatric epilepsy surgery: a bioethical framework

2012· article· en· W2047297625 on OpenAlexaff
George M. Ibrahim, Benjamin W. Barry, Aria Fallah, O. Carter Snead, James M. Drake, James T. Rutka, Mark Bernstein

Bibliographic record

VenueNeurosurgical FOCUS · 2012
Typearticle
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsSickKids FoundationHospital for Sick ChildrenToronto Western Hospital
Fundersnot available
KeywordsPsychosocialEpilepsy surgeryEpilepsyNeurocognitiveMedicineReferralBioethicsDisconnectionPopulationDiseasePsychiatryIntractable epilepsyIntensive care medicineFamily medicineCognitionPolitical science

Abstract

fetched live from OpenAlex

Epilepsy is a common childhood condition associated with a considerable medical and psychosocial burden. Children in whom medical treatment fails to reduce seizure burden represent an especially vulnerable patient population because prolonged, uncontrolled seizures are associated with poor developmental and neurocognitive outcomes. Surgical treatment in the form of cortical resection, functional disconnection, or neuromodulation may alleviate or significantly reduce the disease burden for a subset of these patients. However, there remains a dichotomy between the perceived benefits of surgery and the implementation of surgical strategies in the management of medically intractable epilepsy. The current paper presents an analysis of the bioethical implications of existing inequities in access to pediatric epilepsy surgery that result from inconsistent referral practices and discrepant evaluation techniques. The authors provide a basic bioethical framework composed of 5 primary expectations to inform public, institutional, and personal policies toward the provision of epilepsy surgery to afflicted children.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.126
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.118
GPT teacher head0.416
Teacher spread0.298 · 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

Citations23
Published2012
Admission routes1
Has abstractyes

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