Assembly and evaluation of an inventory of guidelines that are available to support clinical hematology laboratory practice
Bibliographic record
Abstract
INTRODUCTION: Practice guidelines provide helpful support for clinical laboratories. Our goal was to assemble an inventory of publically listed guidelines on hematology laboratory topics, to create a resource for laboratories and for assessing gaps in practice-focused guidelines. METHODS: PubMed and website searches were conducted to assemble an inventory of hematology laboratory-focused guidelines. Exclusions included annual, technical, or collaborative study reports, clinically focused guidelines, position papers, nomenclature, and calibration documents. RESULTS: Sixty-eight guidelines were identified on hematology laboratory practice topics from 12 organizations, some as joint guidelines. The median year of publication was 2010 and 15% were >10 years old. Coagulation topics had the largest numbers of guidelines, whereas some areas of practice had few guidelines. A minority of guidelines showed evidence of periodic updates, as some organizations did not remove or identify outdated guidelines. CONCLUSIONS: This inventory of current practice guidelines will encourage awareness and uptake of guideline recommendations by the worldwide hematology laboratory community, with the International Society for Laboratory Hematology facilitating ongoing updates. There is a need to encourage best guideline development practices, to ensure that hematology laboratory community has current, high-quality, and evidence-based practice guidelines that cover the full scope of hematology laboratory practice.
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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 | 0.106 | 0.375 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.049 | 0.045 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".