Bibliographic record
Abstract
If Alexander Graham Bell were to come back to life and see what the telephone looks like today, compared with his own invention, he would definitely be astonished. It may not even be appropriate to call today's devices “telephones.” They are multitasking gadgets capable of a myriad of other operations. It is astonishing that the Apple iPhone claims over 350 000 specialized applications (better known as “apps”). On the basis of their multitasking capabilities, many have claimed that “smart phones” could have important applications in medicine, including the automatic transmission and sharing of laboratory data, images, and so forth in real time, for more effective patient care (1). In a recent issue of the journal Science Translational Medicine, Haun et al. described a micro–nuclear magnetic resonance (micro-NMR)5 device for the rapid molecular analysis of human tumor samples (2). In the editor's summary, the title was modified to read “A Micro-NMR Smart Phone for Detecting Cancer.” Unfortunately, the editor, in his effort to draw more attention, portrayed the smart phone as an integral part of this futuristic diagnostic device. In this case, however, the smart phone was only a minor player that merely controlled the NMR device, an operation that could probably be performed more conveniently with a remote control or a button on the NMR unit. Nevertheless, we describe this pioneering technology in an effort to realistically evaluate its usefulness and performance as the technology stands today. It is common in the diagnostic and biomarker field for advances like this one to be oversold and for overly optimistic views to be expressed regarding their clinical utility. The phenomenon of declining interest in published reports over time has become known as the “decline effect” (3). Some examples of oversold and subsequently failed cancer biomarkers have recently been discussed (4). Therefore, let's see how this technology works and how it performs in real-world applications.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 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".