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Temporal Trend in Postoperative Seizure Prophylaxis Use in Glioma Patients (P3.128)

2015· article· en· W2544974884 on OpenAlexaff
Sarah Lapointe, Marie Florescu, Chanez Djeffal, Karl Bélanger, Dang Khoa Nguyen

Bibliographic record

VenueNeurology · 2015
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsHôpital Notre-Dame
Fundersnot available
KeywordsMedicineGliomaAnesthesia

Abstract

fetched live from OpenAlex

OBJECTIVE:To characterize practice patterns regarding long-term seizure prophylaxis in gliomas over the last 7 years at our institution. BACKGROUND:In 2000, the AAN practice parameter stated that first generation long-term prophylactic antiepileptic drugs (AEDs) should not be routinely used in newly diagnosed brain tumors. North American data up to 2005 reported deviation from these guidelines. Since then, practice patterns have not been reassessed. DESIGN/METHODS:A retrospective chart review was performed on 578 glioma cases evaluated in a single tertiary care center from 2006-2013. Demographics, tumor characteristics, surgical procedure, seizure rate, and AED(s) use were recorded. Long-term prophylactic AED use was defined as continued use at 3 months post-surgery in the absence of seizures. Patients were divided into three groups at surgery: seizure-free with and without prophylactic AEDs, and seizure-patients. Cox Regression survival analysis was performed on each group. RESULTS:Of 578 patients operated between 2006-2013, 330(57.1[percnt]) were seizure-naïve pre-operatively. Of these 330, 205(62.1[percnt]) received prophylactic AED at surgery. 96/205(46.9[percnt]) were still on AED 3 months post-surgery (median use=58 days; 95[percnt]CI(31-152)). Rate of long-term prophylaxis use decreased by only 13.5[percnt] over 6 years (70.3[percnt]-2006; 56.8[percnt]-2012). Phenytoin was the preferred agent in 2006(98.2[percnt]) with increasing use of levetiracetam over years (2[percnt]-2008; 44.6[percnt]-2012). The only predictive factor for prophylaxis use was complete resection (p=0.007, OR=2.0292, 95[percnt]CI(1.2202, 3.4177)). First seizure rate was similar in both seizure-free populations (p=0.910, HR=0.9627, 95[percnt]CI(0.5153, 1.806)). Seizure-population survived longer than seizure-free populations (p=0.004, HR=0.56, 95[percnt]CI(0.3805,0.8298)), with age and complete resection mainly influencing survival (p’s<0.0001). CONCLUSIONS:From 2006 to 2013, most of our patients were maintained on long-term prophylactic AED, suggesting minimal change in practice pattern since published 2000 guidelines. Corrective measures are hence necessary. An increase in new generation AEDs use for seizure prophylaxis was noted, which may warrant a critical appraisal of recent evidence with these newer AEDs to update guidelines.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.270
Teacher spread0.238 · 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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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