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Record W2137627747 · doi:10.1155/2014/203910

Modified Electroconvulsive Therapy in a Patient with Gastric Adenocarcinoma and Metastases to Bone and Liver

2014· article· en· W2137627747 on OpenAlexaff
Jing Gennie Wang, Brian Milne, Rachel Rooney, Tarit Saha

Bibliographic record

VenueCase Reports in Psychiatry · 2014
Typearticle
Languageen
FieldMedicine
TopicElectroconvulsive Therapy Studies
Canadian institutionsKingston General HospitalQueen's University
Fundersnot available
KeywordsMedicineGastric adenocarcinomaElectroconvulsive therapyAdenocarcinomaGeneral surgeryRadiologyInternal medicineCancer

Abstract

fetched live from OpenAlex

Background. In addition to general anesthesia, muscle relaxants are given prior to electroconvulsive therapy (ECT) in order to prevent musculoskeletal injury. Higher doses of muscle relaxants have been suggested for patients at high risk for bone fractures; however, there are adverse side effects associated with these higher doses. Aims. We present a successful case of ECT to treat chronic major depressive disorder in a 62-year-old woman at high risk of bone fracture due to gastric adenocarcinoma with metastases to bone and liver. Case. Increasing doses of the muscle relaxant succinylcholine (0.45-0.74 mg/kg) were sufficient to prevent musculoskeletal complications throughout the course of 9 bifrontal ECT treatments. Following treatment, the patient reported and demonstrated markedly improved mood and functionality, enabling her transfer to a palliative care facility. Conclusion. Standard doses of succinylcholine were sufficient to mitigate the risk of pathological fractures in this patient with metastatic bone lesions. As there are established risks to using high doses of succinylcholine, with no evidence that higher doses reduce the incidence of fractures in high-risk populations, we suggest taking a conservative approach, using clinical observation and periodic plain radiography to dictate succinylcholine dose titration in such high-risk patients.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.009
GPT teacher head0.247
Teacher spread0.237 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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