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Record W4214703185 · doi:10.14740/jmc3816

Change in Pelvic Incidence Associated With Sacroiliac Joint Dysfunction: A Case Report

2022· article· en· W4214703185 on OpenAlexvenueno aff
Eric Chun‐Pu Chu, Arnold Yu Lok Wong

Bibliographic record

VenueJournal of Medical Cases · 2022
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSacroiliac jointProvocation testPelvisRadiographyPhysical examinationLow back painPhysical therapySurgeryPhysical medicine and rehabilitationRadiology

Abstract

fetched live from OpenAlex

The sacroiliac joint (SIJ) is designed primarily for stability with minute motions. SIJ dysfunction refers to improper movement of the SIJs. Diagnosis and evaluation of SIJ dysfunction are difficult, with use of physical maneuvers and image-guided anesthetic injection. This case report describes a 47-year-old female who experienced right buttock pain and painful limp for approximately 2 months. Standing radiographs revealed inflammatory sclerosis surrounding the right SIJ. Physical examination found tenderness over the right SIJ and positive results in provocation (the distraction, compression, and thigh thrust) tests, compatible with right SIJ dysfunction. Her pain was resolved and gait performance was retrieved following 6-month program of combined thoracolumbar manipulation and rehabilitation exercises. Unexpectedly, change in pelvic incidence (PI) angles was noticed on follow-up radiograph. PI remains more or less fixed throughout adult life since the mobility of the SIJs is considered negligible. The current presentation is designed to explore the significance of PI change. The PI disparity unfolds the possibility of recognizing SIJ dysfunction based on consecutive radiographs.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0050.003
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 head0.080
GPT teacher head0.340
Teacher spread0.260 · 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 designCase report
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

Citations10
Published2022
Admission routes1
Has abstractyes

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