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Factors predicting outcome of surgical treatment of spontaneous spinal hematomas: a retrospective cohort study in four tertiary reference centers

2020· article· en· W2944014549 on OpenAlexaff
Rafael Martínez-Pérez, Igor Paredes, Natalia Rayo, Jorge Díaz Molina, Alfonso Lagares

Bibliographic record

VenueJournal of Neurosurgical Sciences · 2020
Typearticle
Languageen
FieldMedicine
TopicSpinal Hematomas and Complications
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineRetrospective cohort studyCohortTertiary careCohort studySpinal surgerySurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Spontaneous spinal extradural hematoma (SSEH) is a rare but disabling disorder. Most of the previous assumptions regarding the factors that contribute to poor neurological recovery from SSEH are based on small case samples or conditions with similar clinical presentations but different physiopathologies. Our goal was to find the most relevant prognostic factors for neurological recovery in patients suffering SSEH treated with surgical evacuation. METHODS: From a retrospective database of 29 surgical patients with SSEH, several clinical and radiological variables were recorded. These variables were compared between patients with good and poor neurological recovery, considering good as an improvement by at least one point in the ASIA Scale. RESULTS: Among the patients included, morbidity and mortality rate was 6.9% and 3.4%, respectively, with a mean follow-up of 7.1 months. Neurological full recovery was experienced by 33% of the patients included, and 86% of individuals had an improvement in their neurological condition at last follow-up. Lesser intramedullary lesions were significantly associated with greater chances of improvement in ASIA Scale at discharge and at follow-up. Surgical decompression within the first 24 hours of onset of symptoms were correlated with better neurological outcomes at follow-up. CONCLUSIONS: MRI is a powerful tool to predict the neurological outcome in SSEH patients, and it should be considered as an another resource to better know the patients with greater chances of having neurological recovery, especially in cases where the neurological examination is not reliable at the initial exam.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.117
GPT teacher head0.364
Teacher spread0.247 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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