Bibliographic record
Abstract
In this issue of Clinical Chemistry, Mary Lopez and colleagues (1) describe novel methods for isolation of protein-bound peptides from serum and their characterization by mass spectrometry. Lopez et al. used selected peptide combinations to develop a new profiling method for ovarian cancer diagnosis. To put this advance into perspective, I will briefly summarize relevant previous literature on diagnostic applications of serum proteomic and peptidomic profiling by mass spectrometry. Approximately 5 years ago, a new approach for diagnosing ovarian cancer, by use of SELDI-TOF mass spectrometry, was proposed by the coauthors of the article under discussion (2). It was then hypothesized that proteins or protein fragments released by tumor cells or their microenvironment may enter the general circulation. By the use of a SELDI chip, proteins or peptides could be extracted from crude serum and used for diagnostic purposes with the aid of mass spectrometry and a mathematical algorithm. Similar methods have subsequently been used to diagnose numerous other malignancies, such as breast, prostate, bladder, pancreatic, head and neck, lung, liver, and nasopharyngeal cancers, as well as gliomas and melanomas, with impressive diagnostic sensitivities and specificities. This method has enjoyed ample coverage in scientific journals, the media, and international conferences (3).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".