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Record W2396039754 · doi:10.12688/f1000research.7586.1

Recent advances in managing a spinal cord injury secondary to trauma

2016· preprint· en· W2396039754 on OpenAlexaff
Christopher S. Ahuja, Allan R. Martin, Michael G. Fehlings

Bibliographic record

VenueF1000Research · 2016
Typepreprint
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of Toronto
FundersCongress of Neurological Surgeons
KeywordsMedicineNeuroprotectionSpinal cord injuryIntensive care medicineRiluzoleSpinal cordBioinformaticsNeurosciencePhysical medicine and rehabilitationPharmacologyPathologyDisease

Abstract

fetched live from OpenAlex

Traumatic spinal cord injuries (SCIs) affect 1.3 million North Americans, producing devastating physical, social, and vocational impairment. Pathophysiologically, the initial mechanical trauma is followed by a significant secondary injury which includes local ischemia, pro-apoptotic signaling, release of cytotoxic factors, and inflammatory cell infiltration. Expedient delivery of medical and surgical care during this critical period can improve long-term functional outcomes, engendering the concept of "Time is Spine". We emphasize the importance of expeditious care while outlining the initial clinical and radiographic assessment of patients. Key evidence-based early interventions (surgical decompression, blood pressure augmentation, and methylprednisolone) are also reviewed, including findings of the landmark Surgical Timing in Acute Spinal Cord Injury Study (STASCIS). We then describe other neuroprotective approaches on the edge of translation such as the sodium-channel blocker riluzole, the anti-inflammatory minocycline, and therapeutic hypothermia. We also review promising neuroregenerative therapies that are likely to influence management practices over the next decade including chondroitinase, Rho-ROCK pathway inhibition, and bioengineered strategies. The importance of emerging neural stem cell therapies to remyelinate denuded axons and regenerate neural circuits is also discussed. Finally, we outline future directions for research and patient care.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.099
GPT teacher head0.478
Teacher spread0.379 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
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

Citations173
Published2016
Admission routes1
Has abstractyes

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