156 A medical student’s reflections on overdiagnosis and overtreatment as seen in a gynecological case of endometrial hyperplasia
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,014 | 0,010 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,020 | 0,028 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».