MétaCan
Menu
Back to cohort
Record W2165532486 · doi:10.1017/s0317167100013664

Spontaneous Intracranial Hypotension: Recommendations for Management

2013· review· en· W2165532486 on OpenAlexaffvenue
Farnaz Amoozegar, Darryl Guglielmin, William Hu, Denise Chan, Werner J. Becker

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2013
Typereview
Languageen
FieldMedicine
TopicNeurosurgical Procedures and Complications
Canadian institutionsFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineMyelographyLeakMagnetic resonance imagingNeurosurgeryIntracranial HypotensionRadiologyFibrinSurgerySpinal cord

Abstract

fetched live from OpenAlex

A literature search found no clinical trials or guidelines addressing the management of spontaneous intracranial hypotension (SIH). Based on the available literature and expert opinion, we have developed recommendations for the diagnosis and management of SIH. For typical cases, we recommend brain magnetic resonance (MR) imaging with gadolinium to confirm the diagnosis, and conservative measures for up to two weeks. If the patient remains symptomatic, up to three non-directed lumbar epidural blood patches (EBPs) should be considered. If these are unsuccessful, non-invasive MR myelography, radionuclide cisternography, MR myelography with intrathecal gadolinium, or computed tomography with myelography should be used to localize the leak. If the leak is localized, directed EPBs should be considered, followed by fibrin sealant or neurosurgery if necessary. Clinically atypical cases with normal brain MR imaging should be investigated to localize the leak. Directed EBPs can be used if the leak is localized; non-directed EBPs should be used only if there are indirect signs of SIH.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.003
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0030.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.004

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 head0.094
GPT teacher head0.338
Teacher spread0.245 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations55
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences NeurologiquesSame topicNeurosurgical Procedures and ComplicationsFrench-language works237,207