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Record W4386222417 · doi:10.3390/children10091456

Pediatric Spinal Cord Injury: A Review

2023· review· en· W4386222417 on OpenAlexaff
N Cunha, Anahita Malvea, Sarah Sadat, George M. Ibrahim, Michael G. Fehlings

Bibliographic record

VenueChildren · 2023
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicinePsychological interventionSpinal cord injuryQuality of life (healthcare)Multidisciplinary approachIntensive care medicinePresentation (obstetrics)Spinal cordPsychiatryNursingSurgery

Abstract

fetched live from OpenAlex

A spinal cord injury (SCI) can be a devastating condition in children, with profound implications for their overall health and quality of life. In this review, we aim to provide a concise overview of the key aspects associated with SCIs in the pediatric population. Firstly, we discuss the etiology and epidemiology of SCIs in children, highlighting the diverse range of causes. We explore the unique anatomical and physiological characteristics of the developing spinal cord that contribute to the specific challenges faced by pediatric patients. Next, we delve into the clinical presentation and diagnostic methods, emphasizing the importance of prompt and accurate diagnosis to facilitate appropriate interventions. Furthermore, we approach the multidisciplinary management of pediatric SCIs, encompassing acute medical care, surgical interventions, and ongoing supportive therapies. Finally, we explore emerging research as well as innovative therapies in the field, and we emphasize the need for continued advancements in understanding and treating SCIs in children to improve their functional independence and overall quality of life.

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)0.001
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.169
GPT teacher head0.503
Teacher spread0.334 · 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 designSystematic review
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

Citations21
Published2023
Admission routes1
Has abstractyes

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