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Record W4389720641 · doi:10.7759/cureus.50533

The Subjective and Objective Improvement Using Chiropractic Biophysics® Protocols

2023· article· en· W4389720641 on OpenAlexaff
Jason W. Haas, Thomas J Woodham, Paul A. Oakley, Miles O. Fortner, Deed E. Harrison

Bibliographic record

VenueCureus · 2023
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsCanadian Chiropractic AssociationYork University
Fundersnot available
KeywordsMedicineChiropracticLow back painBack painSpinal manipulationLumbosacral jointFailed back surgeryPhysical therapySpinal fusionSurgeryAlternative medicinePathology

Abstract

fetched live from OpenAlex

(CBP®) (Chiropractic BioPhysics, Eagle, USA) technique in alleviating the persistent spine pain syndrome (PSPS) and dysfunction in a 50-year-old female who suffered for many years. The purpose of this study is to provide clinicians with a potential treatment option for failed back surgery syndrome (FBSS) and PSPS that doesn't respond to other treatments. The patient did not receive benefits from pharmaceutical and conservative therapies following a low back lifting injury in 2004. After several years of suffering from widespread spinal pain and dysfunction, she received a lumbosacral pedicle screw surgical fixation. The initial surgery was unsuccessful and a follow-up revision and expansion of the fusion failed to alleviate the pain and dysfunction as well. After treatment using CBP, the patient received subjective, objective, and radiographic improvements with long-term stability measured at follow-up. Given that spine pain and low back pain are the number one cause of disability in the world, having economical, repeatable, and measurable techniques to improve even difficult cases is important for astute clinicians treating spine pain.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.063
GPT teacher head0.381
Teacher spread0.318 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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