Multiple Computer-based Methods of Measuring Joint Space Width Can Discriminate Between Treatment Arms in the COBRA Trial — Update of an Ongoing OMERACT Project
Bibliographic record
Abstract
Previously reported data on 5 computer-based programs for measurement of joint space width focusing on discriminating ability and reproducibility are updated, showing new data. Four of 5 different programs for measuring joint space width were more discriminating than observer scoring for change in narrowing in the 12 months interval. Three of 4 programs were more discriminating than observer scoring for the 0-18 month interval. The program that failed to discriminate in the 0-12 month interval was not the same program that failed in the 0-18 month interval. The committee agreed at an interim meeting in November 2007 that an important goal for computer-based measurement programs is a 90% success rate in making measurements of joint pairs in followup studies. This means that the same joint must be measured in images of both timepoints in order to assess change over time in serial radiographs. None of the programs met this 90% threshold, but 3 programs achieved 85%-90% success rate. Intraclass correlation coefficients for assessing change in joint space width in individual joints were 0.98 or 0.99 for 4 programs. The smallest detectable change was < 0.2 mm for 4 of the 5 programs, representing 29%-36% of the change within the 99th percentile of measurements.
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 | 0.085 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".