Successful adaptation of fever and neutropenia clinical practice guideline in China
Bibliographic record
Abstract
To describe a process for adapting a supportive care clinical practice guideline (CPG) for use in a middle income country setting. We reviewed different approaches for CPG adaptation and created a straight-forward approach for adapting supportive care guidelines for use in Shenzhen, China. The initial CPG to be adapted was for the empiric management of fever and neutropenia (FN) in children with cancer and hematopoietic stem cell transplantation recipients. The steps to be used in adaptation were as follows: review of local guideline; understanding local clinical pathways and contexts through interviews; development of worksheets to facilitate adaptation decisions; deliberation of guideline recommendations in focus groups; and drafting of the adapted FN CPG. After several iterations, stakeholders agreed upon a final adapted guideline. We described an approach to adaptation of a supportive care CPG for the middle income country setting of Shenzhen, China. Although we believe this work has broad applicability, this approach requires rigorous evaluation, both in terms of methodology and the validity of the adapted guideline. Future work will evaluate implementation of the adapted CPG.
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 | 0.080 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".