Poster 417 Tests of the Continuum Theory of Fibromyalgia Using Curve Fitting
Bibliographic record
Abstract
Navraj Randhawa: I Have No Relevant Financial Relationships To Disclose To test the “continuum” theory of fibromyalgia syndrome (FS) using regression, curve estimation, and the computerized Statistical Package for the Social Sciences. Exploratory correlational study using curve fitting. Medical pain clinic. 22 male and 38 female pain patients consecutively referred for medical pain management. Not Applicable. FS New Clinical Diagnostic Criteria. FS Total Symptom Score increased with increases in the anatomical extent of pain following a significant positive linear relationship (P < .0001). With the linear model, pain anatomical spread explained 28 percent of variance in FS total symptom severity scores. Non-linear curve fitting explained 24% (Exponential Model), 24% (Compound Model) and 30% (Power Model) showing no departure from the linear model (no discontinuity). Quadratic and logarithmic models showed no consistent positive versus negative correlation. Examinations of individual clinical features showed no positive linear relationship between pain anatomical extent versus headache or cognitive symptoms, very small positive linear relationships for depression (7% of variance explained), and general fatigue (16% of variance explained), and a small positive linear relationship with waking up tired (22% of variance explained). Non-linear curve fitting did not improve explained variance. The regression curves appeared linear above and below FS diagnosis cut-off scores. These findings support the hypothesis that FS clinical features follow a continuum of severity with pain anatomical extent. Level III
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".