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Reliability and Validity of Accelerometry Methods Used to Assess Facet Joint Crepitus and Cavitation

2017· article· en· W4389021340 on OpenAlexaff
Gregory D. Cramer, Matthew Budavich, Preetam Bora, Terry K. Koo, Dana Madigan, Amanda Braden, Kim Ross

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsCanadian Memorial Chiropractic College
Fundersnot available
KeywordsAccelerometerJoint (building)OrthodonticsMedicineStructural engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

Objective Novel accelerometry methods have been developed to assess and localize vibrations from zygapophyseal (Z) joint audible sounds (crepitus and cavitations) during normal lumbar motion and side‐posture spinal manipulation. This study assessed the reliability and validity of the methods. Methods A lumbar spine was embedded in silicone in the prone position. Before pouring the silicone, tunnels were created from the posterior aspect of the Z joints to the surface. Once the spine phantom was completed, a specialized mechanical device was lowered into each tunnel and was used to strike the Z joints with a force of approximately 7 N to simulate joint crepitus, and 67 N to simulate cavitation (2 experiments). Ten accelerometers applied in a previously developed pattern were used to localize the specific origin of the joint vibrations. For each experiment (crepitus and cavitation), each joint was struck on three different passes (n=30) while recordings from the accelerometers were made. The order of joint strikes was randomized twice for use in two observation sessions. Two observers blinded to the joint strikes, results of one another, and to their previous results, analyzed the oscilloscope recordings of the accelerometers on two separate occasions, separated by a minimum of 2 hours, to identify the joint from which the vibrations originated. Intra‐ and inter‐observer reliability and validity (actual joint struck vs. observer determination from recordings) were calculated. Results Crepitus, Observer 1: validity Test (Run) 1 = 0.93; validity Test 2 = 0.96; intra‐observer reliability (Test 1 vs. Test 2) = 0.96; Crepitus, Observer 2: validity Test 1 = 0.89; validity Test 2 = 0.96; intra‐observer reliability = 0.93; Crepitus, interobserver reliability (Observer 1 vs. Observer 2) = 0.94. Cavitation, Observer 1: validity Test 1 = 1.00; validity Test 2 = 1.00; intra‐observer reliability (Test 1 vs. Test 2) = 1.00; Cavitation, Observer 2: validity Test 1 = 1.00; validity Test 2 = 1.00; intra‐observer reliability (Test 1 vs. Test 2) = 1.00; Cavitation, interobserver reliability (Observer 1 vs. Observer 2) = 1.00. Conclusions The methods as tested in a highly controlled environment were in almost perfect agreement for both simulated crepitus and simulated cavitation. The methods should be further developed and refined for use in human subjec ts . Support or Funding Information NIH/NCCIH Grant # 3R01AT000123‐06S2

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)0.027
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.062
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.144
GPT teacher head0.421
Teacher spread0.277 · 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 designBench or experimental
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".

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Citations1
Published2017
Admission routes1
Has abstractyes

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