The use of standardized order sets to optimize treatment for Guillain-Barre Syndrome: a literature review
Bibliographic record
Abstract
Background: Standardized order sets are thought to improve patient outcomes in multiple ways. They reduce costs without reducing quality of care, and improve efficiency. In both surgical and medical conditions patients benefit from order sets in various disease states. In Guillain-Barre syndrome (GBS), the use of standardized order sets may be beneficial as there are a defined set of disease-specific diagnostic tests and treatments to be implemented. Here, the primary aim was to search for, and evaluate standardized order sets for GBS, and to provide a basis for development of future pathways. Methods: We used the Cochrane, TRIP, and MEDLINE/PUBMED databases, searching between January 1966 and April 2014. Search terms included: “Guillain-Barre Syndrome” and its synonyms, “(standardized) order set”, “clinical pathway”, “neurology” and “admission bundle.” Results: Despite anecdotal evidence of order sets, no formal data has been published showing benefit after implementation of these sets in GBS or any neurological condition. Conclusions: Although evidence exists for use of standardized order sets in surgical and medical settings, no published data exist in neurology. Given GBS has a defined set of disease-specific and state-specific treatment options, a standardized order set used on admission for GBS patients may prove to be beneficial.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".