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Record W3164204818 · doi:10.46292/sci2702-50

Experimental Treatments for Spinal Cord Injury: What you Should Know

2021· article· en· W3164204818 on OpenAlexafffund
Vieri Failli, Naomi Kleitman, Daniel P. Lammertse, Jane Hsieh, John D. Steeves, James W. Fawcett, Mark H. Tuszynski, Armin Curt, Michael G. Fehlings, James D. Guest, Andrew R. Blight

Bibliographic record

VenueTopics in Spinal Cord Injury Rehabilitation · 2021
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsToronto Western HospitalUniversity of TorontoInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersInternational Collaboration on Repair DiscoveriesCraig H. Neilsen FoundationMedical Research CouncilParalyzed Veterans of AmericaChristopher and Dana Reeve Foundation
KeywordsMedicineSpinal cord injuryDistressingSpinal columnTerminologySpinal injuryPhysical medicine and rehabilitationSpinal cordPhysical therapySurgeryPsychiatry

Abstract

fetched live from OpenAlex

Experiencing a spinal cord injury (SCI) is extremely distressing, both physically and psychologically, and throws people into a complex, unfamiliar world of medical procedures, terminology, and decision making. You may have already had surgery to stabilize the spinal column and reduce the possibility of further damage. You are understandably distressed about the functions you may have lost below the level of spinal injury. You wish to recover any lost abilities as soon as possible. You, your family, or friends may have searched the Internet for treatments and cures.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.101
GPT teacher head0.469
Teacher spread0.368 · 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.

Study designOther design
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

Citations20
Published2021
Admission routes2
Has abstractyes

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