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Long-term Follow-up of Surgically Treated AIS Patients

2002· article· en· W5919319 on OpenAlexaff
Doug Hill, V.J. Raso, Karine Moreau, Marc Moreau, J Mahood

Bibliographic record

VenueStudies in health technology and informatics · 2002
Typearticle
Languageen
FieldMedicine
TopicDiagnosis and Treatment of Venous Diseases
Canadian institutionsGlenrose Rehabilitation HospitalCapital District Health Authority
Fundersnot available
KeywordsTerm (time)MedicineComputer scienceSurgeryRadiologyPhysicsAstronomy

Abstract

fetched live from OpenAlex

The aim of this study was to determine the long-term changes in spine and trunk alignment in patients who have undergone scoliosis surgery. Twenty-three (16F; 7M; at age of surgery 15.7 +/- 4.9 years) patients with adolescent idiopathic scoliosis agreed to participate and had posterior-anterior radiographs and surface topography prior to derotation surgery, within 6 months of surgery, at 2 years post-operatively and 5-10 years (mean follow-up period 6.11 +/- 1.6 years) after surgery. Cobb angles, surface trunk rotations, and cosmetic scores were measured at each visit. A questionnaire assessed back appearance and pain at the 5-10 year follow-up. The results of the questionnaire were compared to 25 patients with idiopathic scoliosis who had recently undergone surgery. A paired two-tailed Student's t-test with p=0.05 was used to compare the deformity between visits. The Cobb angle and cosmetic score improved after surgery while the change in trunk rotation was insignificant (p=0.37). Between the two-year and 5-10 year review, the Cobb angle, cosmetic score, and surface trunk rotation significantly increased. Self-perception of appearance and pain were similar to the control group. Surgical correction of scoliosis is not completely maintained during adulthood although the radiographic and surface deterioration does not appear to be clinically significant.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.339
Teacher spread0.291 · 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

Citations7
Published2002
Admission routes1
Has abstractyes

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Same venueStudies in health technology and informaticsSame topicDiagnosis and Treatment of Venous DiseasesFrench-language works237,207