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Record W7133018135

Pre-Surgical Evaluation and Seizure Outcome of Surgical Treatments for Pediatric Drug Resistant Epilepsy

2023· dissertation· W7133018135 on OpenAlexaboutno aff
Omar Yossofzai

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsEpilepsyEpilepsy surgeryDrug Resistant EpilepsyOpen surgeryOutcome (game theory)CohortOpen labelCohort study
DOInot available

Abstract

fetched live from OpenAlex

Approximately one-third of children with epilepsy have drug-resistant epilepsy (DRE), that is, poorly controlled seizures despite treatment with two or more appropriately selected anti-seizure medications. Although open resective surgery is recommended for DRE, there is variability in the reported seizure outcome. Further, it remains an underutilized treatment because of fear or misconceptions of the risk of surgery. A minimally invasive MRI laser interstitial thermal therapy (MRgLITT) has been suggested as an alternative to open surgery, but effectiveness compared to open surgery is unknown. There has also been reports of changing trends in pre-surgical evaluation and epilepsy surgery in Europe and the USA. It is unclear if such changes are observed in Canada. The dissertation will address these aims: (1) evaluate machine learning (ML) algorithms for predicting seizure outcome after open surgery and compare performance of ML to a classical statistical method; (2) compare seizure outcome, complications and length of hospitalization of MRgLITT to open epilepsy surgery (3) evaluate ML models for prediction of seizure outcome following MRgLITT and identify important predictors of seizure freedom and (4) assess trends of pre-surgical evaluation and epilepsy surgery at the Hospital for Sick Children and compare trends in the later period (2014-2019) to an earlier period (2001-2013). We found that ML algorithms performed well for prediction of seizure outcome following open surgery as well as MRgLITT, and performed better than the classical statistical method. Further, ML could identify important predictors of seizure freedom, which could assist with patient selection for these treatments. In the matched cohort of MRgLITT to open surgery patients, MRgLITT had lower seizure-free outcome than open surgery patients but had the advantage of better safety profile and shorter hospital stay. Such findings are important for pre-surgical counselling of families on the benefits and risks of MRgLITT. We found that despite an increasing number of pre-surgical evaluations, there was a reduction in the number of surgeries, particularly in the later period, due to a larger proportion of patients in whom the seizures could not be localized. Understanding the changing trends will help with decisions on allocating healthcare resources.

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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.445
Teacher spread0.389 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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