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Record W2076125025 · doi:10.3389/fnint.2013.00085

Functional testing in animal models of spinal cord injury: not as straight forward as one would think

2013· review· en· W2076125025 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Integrative Neuroscience · 2013
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsSpinal Cord Injury OntarioUniversity of Alberta
FundersNational Institute of Neurological Disorders and Stroke
KeywordsSpinal cord injuryCeiling effectPhysical medicine and rehabilitationMedicinePsychologyNeuroscienceSpinal cordPathology

Abstract

fetched live from OpenAlex

When exploring potential treatments for spinal cord injury (SCI), functional recovery is deemed the most relevant outcome measure when it comes to translational considerations. Yet, assessing such recovery and potential treatment effects is challenging and the pitfalls are frequently underestimated. The consequences are that in many cases positive results cannot be reliably replicated, and likely treatments that appear to lack effects have been dismissed prematurely. In this article we review the relationships between lesion location/severity and functional outcomes with specific consideration given to floor and ceiling effects. The roles of compensatory strategies, the challenges of distinguishing them from bona fide recovery, and of comparing function to pre-injury levels given the variability inherent in animal testing are discussed. Ultimately, we offer a series of considerations to enhance the power of functional analysis in animal models of SCI.

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.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.004
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 head0.240
GPT teacher head0.439
Teacher spread0.200 · 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