The hundred person wellness project and Google’s baseline study: medical revolution or unnecessary and potentially harmful over-testing?
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.
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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 | 0.052 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.063 | 0.068 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".