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In Reply to Brisson and colleagues

2015· letter· en· W947211820 on OpenAlexaffabout
Kevin McLaughlin, Sylvain Coderre

Bibliographic record

VenueAcademic Medicine · 2015
Typeletter
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsConfidentialityObligationInternet privacyPasswordTracking (education)Point (geometry)Medical educationPsychologyHealth recordsMasking (illustration)MedicineComputer scienceHealth carePolitical scienceComputer securityLawPedagogy

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.115
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.073
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.115
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.002
Science and technology studies0.0060.007
Scholarly communication0.0070.010
Open science0.0060.005
Research integrity0.0730.089
Insufficient payload (model declined to judge)0.0100.012

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.

Opus teacher head0.561
GPT teacher head0.609
Teacher spread0.048 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2015
Admission routes2
Has abstractyes

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