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Record W2582065226 · doi:10.3171/2016.10.spine16948

Leptomeningeal dissemination of a low-grade lumbar paraganglioma: case report

2017· article· en· W2582065226 on OpenAlexaff
Nick Thomson, Karel Pacák, Meic H. Schmidt, Cheryl A. Palmer, Karen L. Salzman, Marjan Champine, Joshua D. Schiffman, Adam L. Cohen

Bibliographic record

VenueJournal of Neurosurgery Spine · 2017
Typearticle
Languageen
FieldMedicine
TopicAdrenal and Paraganglionic Tumors
Canadian institutionsInstitute of Population and Public Health
FundersNational Institutes of Health
KeywordsMedicineParagangliomaTemozolomideRadiation therapyRadiologyIntrathecalLumbarThioTEPASurgeryChemotherapy

Abstract

fetched live from OpenAlex

Leptomeningeal dissemination of paraganglioma is rare, with only 2 prior cases in the literature. The authors present the case of a metastatic low-grade lumbar paraganglioma via leptomeningeal dissemination. This report emphasizes the utility of 3,4-dihydroxy-6- 18 F-fluoro- l -phenylalanine ( 18 F-FDOPA) PET scanning for diagnosis, as well as the combination of radiation therapy and alkylating chemotherapeutic agents for the treatment of this rare phenomenon. The patient was a 61-year-old woman who presented with low-back pain and was found to have an isolated L-3 intrathecal tumor on MRI. Sixteen months after gross-total en bloc resection of the paraganglioma, the patient again became symptomatic with new neurological symptoms. MRI findings revealed enhancing leptomeningeal nodules throughout the spine. 18 F-FDOPA PET/CT scanning was used to confirm the diagnosis of disseminated paraganglioma. Intrathecal thiotepa, radiation therapy, and systemic therapy with capecitabine and temozolomide have been used sequentially over a 2-year period, with each able to stabilize tumor growth for several months. The authors also summarize the 2 other reports of leptomeningeal dissemination of paragangliomas in the literature and compare the course and management of the 3 cases.

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)0.002
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 score0.247
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.023
GPT teacher head0.330
Teacher spread0.306 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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