Impairment level SumScore for lower extremity Complex Regional Pain Syndrome type I
Bibliographic record
Abstract
OBJECTIVES: To construct a single indicator on impairment level for lower extremity Complex Regional Pain syndrome type I (CRPS I). DESIGN: The Impairment level SumScore (ISS) for upper extremity CRPS I was adapted to be used for lower extremity evaluation. Medline literature search and research findings were used to adapt the upper extremity version of the ISS, with emphasis on reliability, responsiveness and validity of measurement instruments. Where needed, additional patient data was gathered to evaluate these aspects for different measurement instruments. SETTING: An outpatient clinic of a university hospital in the Netherlands. PARTICIPANTS: Two groups consisting of 17 and 26 healthy volunteers, and two groups of respectively 40 and 18 lower extremity CRPS I patients according to Veldman's criteria. MAIN OUTCOME MEASURES: VAS and McGill pain scores, water displacement volumeter values, and physicians' and patients' assessment of CRPS I severity. RESULTS: A combination of measurements, incorporating pain (VAS and McGill), temperature (infrared thermometer), volume (water displacement volumeter) and active range of motion (universal goniometer), was converted in a single score ranging from 5 to 50. The reliability, as well as the responsiveness was adequate. CONCLUSIONS: The lower extremity ISS permits evaluation of the most prominent symptoms in CRPS I, and can be used to monitor changes in CRPS I.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".