Development and Validation of the Spondyloarthritis Radiography Module for Calibration of Readers Using the Modified Stoke Ankylosing Spondylitis Spine Score
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a reference image module aimed at calibration of readers using the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS) to assess radiographic progression in spondyloarthritis. METHODS: Our working group comprised 6 rheumatologists and 3 musculoskeletal radiologists. The following developmental steps were conducted: (1) review of the literature to identify aspects of the mSASSS requiring methodologic clarity; (2) independent assessment of baseline and 2-year radiographs from 25 patients using the mSASSS (pilot exercise); (3) development of a training module (the Spondyloarthritis Radiography [SPAR] module) that clarifies definitions, rules, and scoring methodology and a set of reference radiographic images; (4) scoring exercise 1 by 6 readers on 39 patients, where baseline and 2-year radiographs were scored blinded to time point; and (5) revision of the SPAR module followed by scoring exercise 2 conducted by the same 6 readers on 35 patients. Reliability of status and 2-year change scores was assessed by the intraclass correlation coefficient (ICC) method. RESULTS: ICCs for change scores for the radiologist reader pair improved from 0.46 to 0.62 after minimal calibration with the SPAR module. Recalibration from exercise 1 to exercise 2 with the SPAR module led to substantial improvement in interreader reliability for change in mSASSS score from ICC 0.44 (range 0.31-0.62) to ICC 0.62 (range 0.34-0.84). Simultaneous assessment of anteroposterior and lateral lumbar radiographs did not enhance reliability or detection of progression. CONCLUSION: Calibration according to the SPAR module led to improved reliability in the scoring of the mSASSS, even for expert readers.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".