A Spectrum of Views on Clinical Mass Spectrometry
Bibliographic record
Abstract
The June 2009 issue of Clinical Chemistry contained our very first Q&A, which has since become a monthly feature in the journal. In that Q&A we asked 5 experts about mass spectrometry (MS)9 in the clinical laboratory. We wanted to find out where we stood and where we needed to be. Not only has it been nearly 7 years since we first asked about clinical MS, but we have devoted the entire January 2016 issue of Clinical Chemistry to this important technology. In this Q&A we ask 6 experts representing instrument design, research, and the clinical laboratory for their perspectives on where we stand in 2016. We were particularly interested in the challenges instrument manufacturers face in meeting the needs of customers and regulatory agencies, the potential of MS moving toward point-of-care (POC) testing, whether there was a next “big thing” in MS on the horizon, and whether MS had matured to the point that it was becoming a true clinical instrument. As scientists involved in instrument development, what demands are manufacturers facing with new applications or instrument designs? How about regulatory hurdles? Reza Javahery: Increased analytical sensitivity, reproducibility, durability (uptime), and ease of use all continue to be features demanded by users. Thus, we cannot focus on just one of these areas. Serviceability is also a major concern. As far as regulatory hurdles, we are still in an environment where there are no clear guidelines. Bradley Hart: As manufacturers, we are tasked with challenges that include improving ease of use and connectivity to automation and laboratory information systems/laboratory information management systems (LIS/LIMS), handling smaller sample sizes and spot samples, improving sensitivity for challenging applications, translating and enabling clinical omics assessment panels, and ultimately providing solutions that enable customers to deliver personalized and precision medicine. In addition, manufacturers of …
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".