Bibliographic record
Abstract
Recently, spectacular advances in diagnostic technologies, genomics, etc. offer unprecedented opportunities for widespread testing of asymptomatic individuals, in the hope that this testing will unravel early disease signs which could lead to preventative or more effective therapeutic measures. In particular, one commercial organization, Theranos, promises to revolutionize diagnostics by offering multi-analyte testing at low prices in commercial outlets, thus challenging the current paradigm of targeted and centralized diagnostic testing. In this paper, I analyze the Theranos technology and their promises, and contrast this information with the currently used technologies, to show that most of the company's claims are exaggerated. While it remains to be seen if this technology will revolutionize diagnostics, in this Opinion Paper, I also draw attention of associated issues, such as self-testing and self-interpretation of results, over-testing, over-diagnosis and over-treatment, along with their associated harms. As the public is bombarded daily with new and revolutionary health-related advances, it is time to balance the enthusiasm of the seemingly obvious huge gains, by also explaining the associated possible harms.
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 | 0.042 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.057 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.015 | 0.029 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".