The hundred person wellness project and Google’s baseline study: medical revolution or unnecessary and potentially harmful over-testing?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Hundred Person Wellness Project is an ambitious pilot undertaking, which aims to intensely monitor 100 individuals over 10 months. Patients with abnormal findings will be treated, in hopes that this early intervention will avoid, or delay, symptomatic disease. Google's "Baseline Study" is of similar scope and will enroll 10,000 people over 2 to 3 years. I here speculate that these approaches will likely not be effective in preventing disease, but instead, lead to unnecessary and potentially harmful interventions. Examples from the cancer screening experience over the last 30 years are provided, which show that intensive testing may uncover indolent disease or incidental findings which, when treated, may cause more harm than good. Additional examples show that aggressive treatments for cancer and other diseases do not always lead to better patient outcomes. I conclude that the recent advances in omics provide us with unprecedented opportunities for high content clinical testing, but such testing should be used with caution to avoid the harmful consequences of over-diagnosis and over-treatment. Despite the detailed rebuttals by Hood and colleagues in another commentary in BMC Medicine, time will show the actual benefits and harms of these ambitious initiatives.
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.
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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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 it