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Low Back Disorders

2017· article· en· W4241147495 on OpenAlexaboutno aff

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2017
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEnvironmental science

Abstract

fetched live from OpenAlex

DESCRIPTION This is an update of a guide to the assessment and treatment of low back pain with an emphasis on evidence-based research. The previous edition was published in 2007. Much of the format and references remain the same, as do many of the illustrations. The section on the use of back belts is identical to the previous edition. However, the exercise handouts and online videos of various tests and techniques are new to this edition. PURPOSE The author states that the purpose is not to perpetuate clinical myths, but to challenge them and propose valid and scientifically justifiable alternatives. As the personal cost, as well as the cost to society in general, of low back pain increases, it is obvious that the care for patients with low back pain needs to improve. The online addition of the treatment and evaluation techniques as performed by the author allows the readers to see the techniques being applied. AUDIENCE This book is intended for any clinician who examines and treats patients with low back pain. Stuart McGill is an internationally recognized authority in spine function, injury prevention, and rehabilitation, as well as a well-published researcher and author. BOOK CONTENT/FEATURES Part one of the book's three parts, on the scientific foundation, has five chapters that examine the research behind low back pain, the anatomy and mechanics of the lumbar spine, and the issue of lumbar spine stability. Part two has two chapters on injury prevention, which do a thorough job of explaining the various methods to assess occupational risk factors. Part three, on rehabilitation, includes the evaluation and development of exercise programs. This section is augmented by videos on the website. Each chapter has a summary section, and key concepts are highlighted in clinical relevance sections. WEBSITE CONTENT/FEATURES Accessing the online ancillaries requires creating an account, which is easy. I had to contact technical support to get into the ancillaries for the book, which was very helpful and responded quickly. The exercises are on PDF files that can be copied and individualized for the patient. Videos demonstrate different exercise and evaluation techniques. ASSESSMENT Accessing the online ancillaries requires creating an account, which is easy. I had to contact technical support to get into the ancillaries for the book, which was very helpful and responded quickly. The exercises are on PDF files that can be copied and individualized for the patient. Videos demonstrate different exercise and evaluation techniques. Rating: ★★★ Reviewer by: Jeff Yaver, Physical Therapy (Kaiser Permanente)

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)0.002
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: none
Teacher disagreement score0.268
Threshold uncertainty score0.898

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.2680.134

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 head0.012
GPT teacher head0.301
Teacher spread0.290 · 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
Published2017
Admission routes1
Has abstractyes

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