Bibliographic record
Abstract
A frequent question among clinical biochemists is how sensitive analytical techniques should be in the context of clinical diagnostics. Currently, the analyte concentrations usually encountered in clinical chemistry range from 10−3 to 10−12 mol/L. By far, the most sensitive nonamplification techniques used in the clinical laboratory are based on noncompetitive immunological assays. Is there any need for measuring analytes at even lower concentrations? The answer is likely yes. Once the methodologies for measuring even lower concentrations of analytes are developed and our knowledge of the many new candidate biological markers that likely will be discovered through the Human Genome Project is more complete, we may be interested in or need to measure analyte concentrations that are 1/10th to 1/100th of those currently measured. Hence, we should continually pursue the development of methodologies that can reach the ultimate sensitivity, i.e., detection of single molecules. In other areas of laboratory medicine, e.g., microbiology, single pathogen particles (e.g., viruses and bacteria) have diagnostic significance. We should not forget that the measurement of a single molecule in a very small fraction of the total blood volume may mean that the whole organism could contain relatively large numbers of such pathogenic or abnormal constituents. When the analytes are nucleic acids (DNA or RNA), PCR and other exponential amplification techniques provide a convenient, yet very powerful, way of amplifying specifically these molecules before final quantification. Unfortunately, proteins are not replicable, and their sensitive quantification usually relies on their interaction with specific reagents, mainly antibodies. An alternative approach to target amplification is the concept of signal amplification, which means that after the analyte interacts with a specific binding reagent, the signal is magnified many-fold to be more easily detected. Various forms of signal amplification have been published, including the use of enzymes, macromolecular carriers, branching reagents, and particles.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".