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Record W4220656894 · doi:10.1159/000522558

Evading Seizures: Phenobarbital Reintroduced as a Multifunctional Approach to End-of-Life Care

2022· article· en· W4220656894 on OpenAlexaff
Helen Senderovich, Sarah Waicus, Keisa Mokenela

Bibliographic record

VenueCase Reports in Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsPhenobarbitalMedicinePhenytoinEpilepsyAnesthesiaPalliative careAnticonvulsantMidazolamPharmacologySedationNursingPsychiatry

Abstract

fetched live from OpenAlex

The selected case study aimed to evaluate the role of phenobarbital as a drug of choice in end-of-life (EOL) settings. Phenobarbital is efficacious in management of EOL seizures and agitation, can be easily administered via different modes, and utilized in various palliative care (PC) settings. Mrs. X., 90-year-old female with a history of glioblastoma multiforme, was a resident of long-term care, residing in a PC unit. She presented with illness progression which resulted in an increased frequency of generalized tonic-clonic seizures which were managed initially with phenytoin. Due to the advanced stage of the illness and significant decline in the patient's cognitive and physical status, oral route and intravenous access were lost, and phenytoin became not an option for seizure control. She was then rotated to subcutaneous phenobarbital, as a result, starting at 30 mg once a day. The dose needed to be titrated up in 15 mg increments to achieve adequate seizure control, and she stabilized on 60 mg of subcutaneous phenobarbital after 2 days. No serious adverse skin reactions were noted with the use of phenobarbital, and it did not abruptly end a patient's life when used at appropriate doses. The sedative properties of phenobarbital had benefited Mrs. X and allowed her to be comfortable approaching EOL with glioblastoma multiforme.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.041
GPT teacher head0.360
Teacher spread0.319 · 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 designCase report
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

Citations5
Published2022
Admission routes1
Has abstractyes

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