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Record W2762871370 · doi:10.3171/2017.5.jns17249

Multilevel, ultra-large-volume epidural blood patch for the treatment of neurocognitive decline associated with spontaneous intracranial hypotension: case report

2017· article· en· W2762871370 on OpenAlexaff
Michael D. Staudt, Stephen Pasternak, Manas Sharma, Sachin Pandey, Miguel Arango, David M. Pelz, Stephen P. Lownie

Bibliographic record

VenueJournal of neurosurgery · 2017
Typearticle
Languageen
FieldMedicine
TopicNeurosurgical Procedures and Complications
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineEpidural blood patchHeadachesOrthostatic vital signsAnesthesiaNeurocognitiveNauseaRefractory (planetary science)Intracranial HypotensionSurgeryBlood pressureInternal medicineComplicationCerebrospinal fluidCognition

Abstract

fetched live from OpenAlex

Spontaneous intracranial hypotension (SIH) is a progressive clinical syndrome characterized by orthostatic headaches, nausea, emesis, and occasionally focal neurological deficits. Rarely, SIH is associated with neurocognitive changes. An epidural blood patch (EBP) is commonly used to treat SIH when conservative measures are inadequate, although some patients require multiple EBP procedures or do not respond at all. Recently, the use of a large-volume (LV) EBP has been described to treat occult leak sites in treatment-refractory SIH. This article describes the management of a patient with profound neurocognitive decline associated with SIH, who was refractory to conservative management and multiple interventions. The authors describe the successful use of an ultra-LV-EBP of 120 ml across multiple levels, the largest volume reported in the literature, and describe the technical aspects of the procedure. This procedure has resulted in dramatic and sustained symptom resolution.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0010.001

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 head0.042
GPT teacher head0.305
Teacher spread0.263 · 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 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

Citations15
Published2017
Admission routes1
Has abstractyes

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