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A model of unloaded human intervertebral disk based on NMR relaxation

2000· article· en· W1990618378 on OpenAlexaff
Teresa Nightingale, Alex MacKay, R. H. Pearce, Kenneth P. Whittall, Borys Flak

Bibliographic record

VenueMagnetic Resonance in Medicine · 2000
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsResidue (chemistry)ProteoglycanChemistryGlycosaminoglycanIntervertebral diskNucleusNuclear magnetic resonanceT2 relaxationIntervertebral discBiophysicsAnatomyBiochemistryMagnetic resonance imagingExtracellular matrixBiologyPhysics

Abstract

fetched live from OpenAlex

NMR relaxation rates were related to the composition of the nucleus pulposus from 11 and anulus fibrosus from six human intervertebral disks. Tissue water was proportional to glycosaminoglycan (GAG) and residue, the noncollagen, non-GAG portion of the dry weight (R2 = 0.74). The solid signal fraction depended on collagen and residue protons (R2 = 0.89). 1/T1 was proportional to collagen and residue (R2 = 0.97). T2 showed 2-4 components labeled A, B, C, and D, with means +/- standard deviations of 3.1 +/- 1.6, 17.5 +/- 9.5, 64 +/- 22, and 347 +/- 162 msec. Signal fractions of A and B depended on the collagen-associated water protons (R2 = 0.94 and 0.85), C on residue-associated water protons (R2 = 0.82), and D on GAG-associated water protons (R2 = 0.74). The data led to a model of disk architecture in which the collagen and residue were largely solid, forming distinct water compartments; the remaining water was present in a proteoglycan gel.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.038
GPT teacher head0.309
Teacher spread0.271 · 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 designSimulation or modeling
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
Published2000
Admission routes1
Has abstractyes

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