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Record W2016532250 · doi:10.1186/1748-7161-7-s1-p22

Algorithms to prescribe Schroth exercises for each of four Schroth curve types

2012· article· en· W2016532250 on OpenAlex
EM Watkins, S Bosnjak, EC Parent

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScoliosis · 2012
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsAlberta Health ServicesUniversity of Alberta
Fundersnot available
KeywordsChecklistMedicineStandardizationExercise prescriptionMedical prescriptionIdiopathic scoliosisScoliosisPhysical therapyMedical physicsPhysical medicine and rehabilitationComputer sciencePharmacologyPsychologySurgery

Abstract

fetched live from OpenAlex

Background Systematic reviews have shown that most exercise studies for scoliosis treatment lacked standardization of exercise prescription. Schroth exercise prescription is based on scoliosis curve type with specific exercises designed to target different aspects of the spinal curve and different areas of the body. The intensity of exercises is increased based on patient capacity. There may be dose dependant and exercise specific effects, therefore it is important to adopt a standardized method of prescription, especially in clinical research trials. Goal: To describe prescription algorithms and a performance checklist for standardizing Schroth exercise treatment based on instructions in the Schroth training.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.079
GPT teacher head0.350
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