Predictive value of symptom level measurements for complex regional pain syndrome type I
Bibliographic record
Abstract
The validity with respect to presence or absence of CRPS I according to Veldman's criteria was assessed for measured pain, temperature, volume differences and limitations in range of motion. Evaluated were 155 assessments of 66 outpatients, initially diagnosed with CRPS I, but many of them not so on follow up visits. Pain was measured with VAS and McGill, temperature by infrared thermometry, volume differences by water displacement volumeters and limitations in range of motion by universal goniometers. Sensitivity, specificity, positive and negative predictive value of the measurement instruments at different cut-off points was calculated. Combined symptom scores were evaluated in a similar fashion. High sensitivity was found for the VAS, McGill, and range of motion. The specificity was overall lower, but highest values were obtained for volume differences. The positive predictive value was good for all measurement instruments. Negative predictive value was lower, especially for measurement of temperature and volume asymmetries. If sensitivity and specificity are equally important, VAS>3 cm, McGill>6 words, temperature difference>or=0.4 degrees C, volume difference>6.5% and ROM limitation>15% provide the best results. Using these cut off values, the highest value of sensitivity and of sensitivity and specificity combined, was found for a combination of VAS, McGill and ROM. The highest value of specificity was found for different combinations of 3, 4 and 5 instruments, all containing the VAS. We conclude that the measured pain, temperature, volume and range of motion can be used as diagnostic indicators for establishing presence or absence of CRPS I.
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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 | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".