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 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 | 0.313 | 0.283 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".