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Record W2268543354 · doi:10.14740/jocmr2472w

Posterior Reversible Encephalopathy Syndrome Resolving Within 48 Hours in a Normotensive Patient Who Underwent Thoracic Spine Surgery

2016· article· en· W2268543354 on OpenAlexvenueno aff
Kunal Vakharia, Ioannis Siasios, Vassilios G. Dimopoulos, John Pollina

Bibliographic record

VenueJournal of Clinical Medicine Research · 2016
Typearticle
Languageen
FieldMedicine
TopicNeurological Complications and Syndromes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMagnetic resonance imagingPosterior reversible encephalopathy syndromeSurgeryHeadachesLesionSpinal cord compressionPercutaneousRadiologySpinal cord

Abstract

fetched live from OpenAlex

Posterior reversible encephalopathy syndrome (PRES) usually manifests with severe headaches, seizures, and visual disturbances due to uncontrollable hypertension. A patient (age in the early 60s) with a history of renal cell cancer presented with lower-extremity weakness and paresthesias. Magnetic resonance imaging (MRI) of the thoracic spine revealed a T8 vertebral body metastatic lesion with cord compression at that level. The patient underwent preoperative embolization of the tumor followed by posterior resection and placement of percutaneous pedicle screws and rods. Postoperatively, the patient experienced decreased visual acuity bilaterally. Abnormal MRI findings consisted of T2 hyperintense lesions and fluid-attenuated inversion recovery changes in both occipital lobes, consistent with the unique brain imaging pattern associated with PRES. The patient's blood pressure was normal and stable from the first day of hospitalization. The patient was kept on high-dose steroid therapy, which was started intraoperatively, and improved within 48 hours after symptom onset.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.253
GPT teacher head0.491
Teacher spread0.238 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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