Withholding diagnostic and prognostic information from patients in Pakistan: ethical and cultural perspectives
Bibliographic record
Abstract
Dear Editor, I am writing to highlight a critical and often overlooked issue in our medical practice: withholding diagnostic and prognostic information from patients in favour of communicating primarily with family members. This practice raises significant ethical and legal concerns that merit urgent discussion. A significant portion of Pakistan's population—about 61%—resides in rural areas, where the literacy rate is approximately 59% [2,3]. Additionally, socio-economic factors play a major role, with over 37% of the population living below the poverty line [1]. Research indicates that patients with low literacy levels experience poorer health outcomes, including lower health knowledge, worse disease markers, higher morbidity, poorer general health status, and increased use of health resources. These patients are generally 1.5 to 3 times more likely to experience adverse health outcomes [4]. Consequently, medical decisions and treatment plans are frequently deferred to the household's primary earner rather than the patient, undermining the patient's autonomy. In countries such as the United States and Canada, patient autonomy is a fundamental principle of medical practice. Patients have the right to be fully informed about their health conditions and make care decisions. In his seminal work The Silent World of Doctor and Patient, bioethicists like Jay Katz argued that excluding patients from decision-making processes is a profound affront to their dignity and autonomy. Similarly, Beauchamp and colleagues, in Principles of Biomedical Ethics, stress the importance of respecting patient autonomy alongside beneficence and nonmaleficence. A recent cohort study concluded that when medical decision-making was conducted through shared decision-making, daily controller therapy adherence in patients with asthma was 84%, a considerable achievement [5]. Respecting patient autonomy does not mean indiscriminately sharing all information but fostering a trust-based relationship where the patient’s preferences for receiving information and involvement in clinical decisions are carefully considered. Healthcare providers must prioritize effective communication and uphold ethical standards by ensuring that patients are fully informed about their diagnoses and involved in their treatment plans. Educational institutions and healthcare organizations should emphasise the importance of these skills in medical training and continuing education programmes. I urge my fellow healthcare professionals to commit to promoting patient autonomy and transparency, ensuring that our patients are respected and their rights upheld in all aspects of their care.
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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 | 0.009 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.017 | 0.027 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".