Quality standards and internal quality control practices in medical laboratories: an IFCC global survey of member societies
Bibliographic record
Abstract
OBJECTIVES: The trueness and precision of clinical laboratory results are ensured through total quality management systems (TQM), which primarily include internal quality control (IQC) practices. However, quality practices vary globally. To understand the current global state of IQC practice and IQC management in relation to TQM the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Task Force on Global Laboratory Quality (TF-GLQ) conducted a survey of IFCC member countries on IQC practices and management. METHODS: The survey included 16 questions regarding IQC and laboratory TQM practices and was distributed to IFCC full and affiliate member countries (n=110). A total of 46 (41.8 %) responses were received from all regions except North America. RESULTS: Of the responding countries, 78.3 % (n=36) had legislative regulations or accreditation requirements governing medical laboratory quality standards. However, implementation was not mandatory in 46.7 % (n=21) of responding countries. IQC practices varied considerably with 57.1 % (n=28) of respondents indicating that they run 2 levels of IQC, 66.7 % (n=24) indicating they run IQC every 24 h and 66.7 % (n=28) using assay manufacturer IQC material sources. Only 29.3 % (n=12) of respondents indicated that every medical laboratory in their country has written IQC policies and procedures. By contrast, 97.6 % (n=40) of responding countries indicated they take corrective action and result remediation in the event of IQC failure. CONCLUSIONS: The variability in TQM and IQC practices highlights the need for more formal programs and education to standardize and improve TQM in medical laboratories.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".