Bibliographic record
Abstract
We would like to thank Brisson and colleagues1 for their letter in reply to our Commentary. Clearly, we agree on the learning potential of patient tracking in the electronic health record (EHR) and that the primacy of a patient’s right to privacy (both in the present and future) and our obligation of confidentiality should not be com promised in the pursuit of learning. However, it is still unclear to us why they feel that distinct guidelines are required to guide behavior when a specific group (medical students) accesses a particular source of data (EHR) at a certain time point (in the future). When students begin training we anticipate that their clinical skills will not be commensurate with those of a practicing physician, but we do not have differential expectations on confidentiality and professionalism.2 Having distinct guidelines to guide the ethical behavior of medical students implies distinct expectations, which is not the case. When a patient confides in a physician or physician-in-training, this information can be stored in various locations, including an EHR, paper charts, and the long-term memory of the physician/trainee. Having distinct guidelines implies privacy risk for electronic data that is somehow absent for data stored elsewhere. Admittedly, the privacy risk of electronic data may be different (remote access may increase risk, whereas the ability to password protect data and identify users might reduce the risk)—but privacy risk is not unique to data stored electronically. Brisson and colleagues raise the possibility that by accessing future events, students may uncover sensitive information. But this risk is not unique to students or to the future—and whether or not this constitutes “snooping” depends upon motivation. Most of us have experienced a situation where we inadvertently discovered sensitive information that was not relevant to our clinical task. Irrespective of how and when we acquire these data, there is the same expectation of privacy and confidentiality. If we were to access data with the intention of uncovering sensitive information that had no clinical or educational merit, then we would be guilty of snooping—which is clearly unethical, regardless of the source of data or our level of training. When completing clinical rotations we sign over patients for whom the diagnosis has not yet been made and/or the response to treatment established. From our own experience, and in discussion with our colleagues, it is common practice to ruminate on cases, ask colleagues for updates, and review progress via paper and electronic charts. Rather than snooping, the primary driver of information-seeking behavior is our need for cognition.3,4 And, while we appreciate that individuals may behave differently when online versus in person,5 we should still be capable of meeting our learning needs without compromising the privacy needs of patients. Kevin McLaughlin, MB ChB (Hons), PhD Assistant dean of undergraduate medical education, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; [email protected] Sylvain Coderre, MD, MSc Associate dean of undergraduate medical education, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
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 | 0.013 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.073 | 0.089 |
| Insufficient payload (model declined to judge) | 0.010 | 0.012 |
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".