Bibliographic record
Abstract
ANSWER One of the primary obligations of a doctor-patient relationship is confi dentiality. Legislation and College policies in most jurisdictions emphasize this obligation. Doctors also have a duty of care to the patients they see. That duty of care includes obtaining an adequate history, performing an adequate examination, and advising patients appropriately. It is important to remember that the Narcotic Control Regulations require patients to tell doctors what narcotics they have obtained in the last 30 days. It is equally important for doctors to inquire, before prescribing narcotics, whether patients have recently received any narcotics, and if so, what kind and how much. Patients’ answers to these questions should be clearly documented. These questions should, of course, be asked of every patient and about all medications before prescribing, but documenting the question and a negative answer from the patient is very important in this situation. Although patients are not always forthright about drug use, you are not expected to be a private investigator. You are, however, expected to ask the appropriate questions and take appropriate action. If you were to have evidence, for example from a hospital emergency department or a walk-in clinic, that a patient was lying, you should consider confronting him or her with that evidence. If you are not reassured by the response, you might want to inform the patient of your and his or her legal obligations and refuse to prescribe the medication. Some patients are labeled “drug seekers.” This can interfere with objective assessment of their health. A patient who has sought drugs can develop genuine symptoms; for example, a patient claiming he or she has renal colic to get narcotics might, on a subsequent visit, suffer from right lower quadrant pain due to appendicitis. The duty of care requires that doctors assess each patient appropriately. Failure to make the correct diagnosis might lead to a bad clinical outcome for a patient and subsequently to a lawsuit against the doctor that could be diffi cult to defend. Dealing with apparent drug seekers is always diffi cult. You cannot report to the police because that would be a breach of confi dentiality, and you can terminate your care of a patient only if that patient’s condition is not medically urgent or you have made arrangements for another physician to take care of him or her. If you prescribe only those drugs you believe necessary for a patient’s condition, however, you will fulfi l your responsibility to that patient.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.089 | 0.035 |
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".