156 A medical student’s reflections on overdiagnosis and overtreatment as seen in a gynecological case of endometrial hyperplasia
Bibliographic record
Abstract
When we enter medical school, we bring preconceived notions about various diseases and their treatments with us. Sometimes, despite rigorous coursework, these ideas follow us into clerkship and even residency. One example of this is junior trainees’ perceptions of cancer and its prognosis. Perhaps because of how it is portrayed socially, it seems many medical students assume all forms of cancer have a poor prognosis and that treatment should always be immediate and aggressive. While there are certainly cancers for which this is true, there are some related conditions for which this approach may do more harm than good. In Obstetrics and Gynecology (OBGYN), there remains a fair bit of controversy surrounding proper diagnosis and management of endometrial hyperplasia (EH). EH is a gynecological condition in which the endometrium, the lining of the uterus, thickens. Although this condition on its own is not cancer, it exists on a spectrum (e.g. simple or complex hyperplasia without atypia versus atypical hyperplasia) that in some cases can lead to cancer of the uterus. Research has shown that pathologists are particularly likely to over-diagnose EH at the low end of the spectrum as a more advanced form suggestive of cancer. Subsequently, upon receiving a diagnosis of ‘hyperplasia,’ gynecologists are likely to recommend invasive and permanent procedures, like hysterectomy, for patients whose relatively benign form of EH may have been better served with risk factor reduction or hormonal therapy. The combination of overdiagnosis and overtreatment results in unnecessary testing and management that at best inconveniences patients and at worst causes new or worsening health concerns and increased healthcare costs. This concept was explained to me in my preclerkship classes, but its significance did not register until I met a patient whose story struck me during my core OBGYN rotation. A woman in her 40’s had undergone a premature hysterectomy upon receiving a diagnosis of EH. On top of expected side effects (e.g. early menopause), post-operative complications included significant lymphedema, which was initially missed and subsequently infected. The resulting sepsis caused a number of complications, including kidney failure and gangrene of the right big toe, requiring amputation. This was clearly a complex case in which medical malpractice played a role, but nevertheless, it highlights how important it is to address issues of overdiagnosis that may be rooted in preconceived notions of how certain conditions should be treated. The above case also raises an interesting predicament regarding medical training. Although my involvement in this case improved my personal understanding of overdiagnosis and its consequences, my peers did not and may never benefit from such an experience. This scenario begs us to ask ourselves the following: How can we better teach medical trainees to walk the fine line between diagnosing those who would benefit from treatment without over-diagnosing those who may suffer from it? Patient panels and lab simulations may be promising tools. In reality, there is unlikely to be a good, let alone great, approach, but I believe further discussion on this topic is worth having.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.020 | 0.028 |
| Insufficient payload (model declined to judge) | 0.007 | 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".