Bibliographic record
Abstract
Imagine if patients could go to their computers, click on a medical-services Web site and order their own blood work or liver function test. They would then report to a clinic to have the necessary work done, and later read the results online. The tests were ordered and carried out with no physician involvement. Imagine no more, because direct access testing (DAT) is the newest development to hit the online medical-services industry. With $40 billion being spent on lab tests in the US every year, it's little wonder companies are trying to seek some of this business online. Last February, Quest Diagnostics, Inc., the world's largest laboratory company, received permission from 4 US states to open storefront clinics. Its Quest Test system (www.questest.com) then allowed customers to order their own tests, go to a company clinic to get blood drawn, and then get the results online without ever seeing a doctor. As Ken Freeman, the company's chair and CEO, states, its goal is to “teach you about taking control of your most important asset — your health.” At the site, customers are presented with several options; at the “e-commerce” side, for instance, a secure transaction window appears with a list of tests that can be purchased. Each test is described and a price is displayed. The test can then be added to the patient's “shopping cart,” and at the end of the shopping session the bill is paid via credit card. (In October the company was offering $5 off a liver health panel in support of National Liver Awareness Month.) Other sites sell home test kits. Craig Medical Diagnostic Tests (www.craigmedical.com) sells home-use medical tests; customers can order everything from ovulation prediction kits to home urinalysis tests. The test packages are mailed to the buyer. Home Medical Tests Mall (www.homemedicaltestsmall.com) offers a similar service. Canadians can already buy test packages from the last 2 companies, but regulations currently restrict the sale of full DAT services. It may be only a matter of time before that changes.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.813 | 0.757 |
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".