Bibliographic record
Abstract
There is much more research that describes the measurement properties of evaluative measures such as the Roland-Morris (RM) scale1 today than there was a decade ago. The greater volume of studies provides more data that can be used to shape clinical decisions. This increased amount of research also increases the chance that the results of some studies, at times, may conflict with results of other studies. As the number of studies on a particular issue grows, the potential for conflicting results increases. The study of Davidson and Keating2 seems to be an illustration of this phenomenon. Davidson and Keating2 examined the reliability and responsiveness of 5 functional status questionnaires designed for patients with low back pain (LBP). One of the scales examined was the RM scale, a questionnaire that has been studied extensively by our group and many others. Davidson and Keating found that the reliability of RM scale measurements was low, with an intraclass correlation coefficient (ICC [2,1]) of .53 (95% confidence interval [CI]=.29,.71) for a sample of 47 patients with LBP who reported that their LBP was “about the same,” “a little better,” or “a little worse.” For a smaller subgroup that reported their LBP was “about the same,” the ICC (2,1) was lower at .42 (95% CI=−;.07, .75). Based in part on these findings, the authors concluded that the RM scale “appeared to lack sufficient reliability and scale width for clinical application.”2(p8)
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 | 0.009 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".