National Academy of Clinical Biochemistry Laboratory Medicine Practice Guidelines for Use of Tumor Markers in Clinical Practice: Quality Requirements1
Bibliographic record
Abstract
BACKGROUND: This report presents updated National Academy of Clinical Biochemistry Laboratory Medicine Practice Guidelines summarizing quality requirements for the use of tumor markers. METHODS: One subcommittee developed guidelines for analytical quality relevant to serum and tissue-based tumor markers in current clinical practice. Two other subcommittees formulated recommendations particularly relevant to the developing technologies of microarrays and mass spectrometry. RESULTS: Prerequisites for optimal use of tumor markers in routine practice include formulation of the correct clinical questions to ensure selection of the appropriate test, adherence to good clinical and laboratory practices (e.g., minimization of the risk of incorrect patient and/or specimen identification, tube type, or timing), use of internationally standardized and well-characterized methods, careful adherence to manufacturer instructions, and proactive and timely reactions to information derived from both internal QC and proficiency-testing specimens. Highly desirable procedures include those designed to minimize the risk of the reporting of erroneous results attributable to interferences such as heterophilic antibodies or hook effects, to facilitate the provision of informative clinical reports (e.g., cumulative and/or graphical reports, appropriately derived reference intervals, and interpretative comments), and when possible to integrate these reports with other patient information through electronic health records. Also mandatory is extensive validation encompassing all stages of analysis before introduction of new technologies such as microarrays and mass spectrometry. Provision of high-quality tumor marker services is facilitated by dialogue involving researchers, diagnostic companies, clinical and laboratory users, and regulatory agencies. CONCLUSIONS: Implementation of these recommendations, adapted to local practice, should encourage optimization of the clinical use of tumor markers.
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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 | 0.193 | 0.254 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.012 | 0.007 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".