Bibliographic record
Abstract
CONSIDER THIS IRONY OF MODERN LIFE: IN A MEDICAL CRISIS, EMERGENCY physicians would have an easier time accessing a patient’s bank account using his or her automatic teller machine card than they would finding critical medical history using his or her medical insurance card. Medical records, including crucial electrocardiograms, drug allergies, or medical conditions, are typically stored on paper and are often inaccessible in emergencies. The ability to access medical charts electronically, in emergency situations or in routine medical settings, has not paralleled the growth of financial networks or indeed of the Internet. Although several commercial sites are now selling space for individuals to put their medical records online and numerous institutions have local electronic medical records (EMRs) in place, most clinical records are still kept in paper charts that are stored at a single location. The challenge of building an integrated EMR system has proved to be more than technological; 25 years of attempts to formalize the terms and concepts of medical practice has exposed some fascinating philosophical conundrums. What belongs in a medical record, and how should medical conditions or ideas be encoded? Which tasks are best performed by physicians, and which by the computer? Is it possible to encapsulate the medical encounter in digital form? A number of centers have had local EMRs available for decades, providing evidence that thoughtfully implemented EMRs improve medical care through adjunct technology like error checking and allow easier study of trends in a clinic population. New links are being forged between individual patient data and the information in digital libraries or the tools of computerized decision support. While the potential for ease of access and error reduction seems obvious, new technologies should be held to the same standards of evidence as new treatments are. Research in this field has started to look not only at efficiency and institutional satisfaction but also at health outcomes and impact on the patient-physician relationship. As researchers measure the gains made by using EMRs, they should also consider potential losses. Will physicians rely too heavily on the safety nets of automatic warning systems, losing the ability to think through the problem—just as many who rely on calculators cannot compute answers on their own? With full histories available at the touch of a button, will tired interns and residents cut corners, neglecting to ask their own questions? EMRs must be a tool for improving patient care rather than a crutch or a hindrance to the primary work of caring for patients. This month, MSJAMA examines the legal, ethical, and technical challenges of EMRs. With a new generation of physicians accustomed to working with computer technology, we may see some of the promise of the past 3 decades of research in this field come to fruition in the coming years.
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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.015 | 0.043 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.048 | 0.033 |
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".