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The “Myths” of Low Back Pain

2004· article· en· W1967450598 on OpenAlexaff
Camilla Ihlebæk, Hege R. Eriksen

Bibliographic record

VenueSpine · 2004
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsMedicineNorwegianMythologyPopulationBack painLow back painChiropracticGeneral practicePhysical therapyFamily medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: In 2001, several myths of low back pain still were alive in the general population in Norway, myths that were not in concordance with current guidelines. OBJECTIVES: To investigate perceptions about back pain in Norwegian general practitioners and physiotherapists and to compare these with perceptions in the general population. METHODS: During June 2001, 436 general practitioners (mean age 44.8, range 26-69 years) and 311 physiotherapists (mean age 47.6, range 25-70) were asked to rate their agreement with 7 statements, corresponding to Deyo's 7 myths that formulate 7 common misbeliefs on back pain. The corresponding data from the general population of 807 individuals (mean age 45.5, range 25-70) were sampled during early spring 2001. RESULTS: There were significant differences between the general population, general practitioners, and physiotherapists for all myths, the general population being more likely to agree with all myths. The differences were maintained even after controlling for educational level in the general population. There were no differences between general practitioners and physiotherapists except for the myths "radiographs and newer imaging tests can always identify the cause of pain" and "back pain is usually disabling," whereas general practitioners were less likely to disagree with the myths. Few gender and age differences were found in the professional groups. CONCLUSION: In Norwegian general practitioners and physiotherapists, Deyo's 7 myths mostly seem to be dead and buried. However, it does not seem that this has extended to the public yet, as many myths still are alive in the general population.

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)0.008
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.015
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.261
Teacher spread0.255 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations27
Published2004
Admission routes1
Has abstractyes

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