A guide to conducting systematic reviews of clinical laboratory tests
Bibliographic record
Abstract
Clinical laboratory professionals have an instrumental role in supporting clinical decision making with the optimal use of laboratory testing for screening, risk stratification, diagnostic, prognostic, treatment selection and monitoring of different states of health and disease. Delivering evidence-based laboratory medicine relies on review of available data and literature. The information derived, supports many national policies to improve patient care through clinical practice guidelines or best practice recommendations. The quality, validity and bias of this literature is variable. Hence, there is a need to collate similar studies and data and analyse them critically. Systematic review, thus, becomes the most important source of evidence. A systematic review, unlike a scoping or narrative review, involves a thorough understanding of the procedure involved and a stepwise methodology. There are nuances that need some consideration for laboratory medicine systematic reviews. The purpose of this article is to describe the process of performing a systematic review in the field of laboratory medicine, describing the available methodologies, tools and software packages that can be used to facilitate this process.
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 | 0.131 | 0.291 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.019 | 0.024 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.087 | 0.025 |
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".