Delphi study on the STRIDE algorithm for compression selection in upper-body lymphoedema
Bibliographic record
Abstract
Background: The original STRIDE algorithm covered lower-limb lymphoedema but not the upper body. Aims: To update the STRIDE algorithm for compression selection to treat lymphoedema of the upper limb, breast and trunk by achieving consensus on the definitions and importance of its six aspects. Method: Using a modified Delphi framework, clinical experts in the field ranked agreement and gave open-ended feedback over two rounds of surveys, with a >70% threshold for agreement. Results: In the first round, participants represented five continents (n=36). Characteristics that met the threshold consensus of >70% agreement were then applied to the STRIDE algorithm, and the second survey was developed. In the second round (n=22), the definitions of all elements of the STRIDE algorithm had at least 70% agreement or strong agreement. Shape and issues were the elements most often considered first in compression selection, while refill was least often considered first in selection. Conclusions: This Delphi study achieved consensus on the descriptions of the elements of the revised STRIDE algorithm for compression in upper-limb, breast and trunk lymphoedema. The STRIDE algorithm can now be used to make clinical decisions on selecting compression garments for the upper body.
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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 | 0.000 |
| 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) | 0.000 | 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".