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International Standards for Neurological Classification of Spinal Cord Injury: Cases with classification challenges

2014· article· en· W4246577567 on OpenAlexaff
Steven Kirshblum, Fin Biering‐Sørensen, Randal R. Betz, Stephen P. Burns, William H. Donovan, Daniel Graves, Mark K. Johansen, Linda Jones, M.J. Mulcahey, Gianna M. Rodriguez, Mary Schmidt-Read, John D. Steeves, Keith E. Tansey, William Waring

Bibliographic record

VenueJournal of Spinal Cord Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
Fundersnot available
KeywordsSpinal cord injuryDocumentationMedicineInternational Classification of Functioning, Disability and HealthInternational standardMedical emergencyPhysical therapyComputer scienceSpinal cordPsychiatryRehabilitation

Abstract

fetched live from OpenAlex

The International Standards for the Neurological Classification of Spinal Cord Injury (ISNCSCI) is routinely used to determine the levels of injury and to classify the severity of the injury. Questions are often posed to the International Standards Committee of the American Spinal Injury Association regarding the classification. The committee felt that disseminating some of the challenging questions posed, as well as the responses, would be of benefit for professionals utilizing the ISNCSCI. Case scenarios that were submitted to the committee are presented with the responses as well as the thought processes considered by the committee members. The importance of this documentation is to clarify some points as well as update the SCI community regarding possible revisions that will be needed in the future based upon some rules that require clarification.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.191
GPT teacher head0.465
Teacher spread0.274 · 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 designNon-randomized trial
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

Citations87
Published2014
Admission routes1
Has abstractyes

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