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Record W4392292987 · doi:10.7759/cureus.55197

A Case of Type 2 Diastematomyelia With Spina Bifida in a Pediatric Female Patient

2024· article· en· W4392292987 on OpenAlexaboutno aff
Shivani S Bothara, Pratap Parihar

Bibliographic record

VenueCureus · 2024
Typearticle
Languageen
FieldMedicine
TopicSpinal Dysraphism and Malformations
Canadian institutionsnot available
Fundersnot available
KeywordsDiastematomyeliaMedicineSpina bifidaMagnetic resonance imagingLaminectomySpinal cordSpinal dysraphismIntervention (counseling)SurgeryPediatricsRadiologyNursing

Abstract

fetched live from OpenAlex

This case report presents the clinical and radiological findings of a seven-year-old female with type 2 diastematomyelia and spina bifida, emphasizing the complexity of congenital spinal anomalies in pediatric patients. The patient presented with a two-month history of lower back pain, prompting diagnostic investigations. Radiographic examination revealed spina bifida at the L3-L5 levels, subsequently confirmed by magnetic resonance imaging (MRI), which disclosed bifid spinous processes, an absent posterior arch, and a split spinal cord terminating at the L3-L4 disc levels. The Vancouver classification system facilitated a standardized characterization of congenital spinal anomalies. The multidisciplinary approach involving orthopedic and neurosurgical specialists led to a conclusive diagnosis of type 2 diastematomyelia with simple spinal dysraphism. Surgical intervention, encompassing laminectomy and correction of the split spinal cord, was successfully performed, resulting in the stabilization of the patient. This case underscores the importance of early diagnosis, advanced imaging modalities, and collaborative management in addressing rare congenital spinal anomalies. The discussion delves into the clinical implications, diagnostic challenges, and the pivotal role of surgical intervention. Insights from this case contribute to the existing literature, guiding healthcare professionals in understanding and managing similar cases with potential implications for future research and treatment strategies.

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 score0.109
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.019
GPT teacher head0.281
Teacher spread0.262 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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