Drowning in ethics for low‐risk data: is this best for patients?
Notice bibliographique
Résumé
As clinicians, all practitioners should review their practice. Audit is well mandated, at least for urologists; however, what does the public expect from us? Certainly, we are aware that plenty of other groups, be they government, regulators or others, are watching 1. Increasingly, beyond our control, patients may also rate our performance individually via social media with no recourse concerning ‘fake’ data 2. This is not the best form for patient-reported outcomes. However, there is little else out there to judge if we are at a benchmark, beyond or below it. So how do we control outcome data and present it in a manner that is ethical, confidential and timely? If we asked a patient if it was a good idea to audit results, present them and learn from them, they would probably call this ‘best practice’ 3. The Royal Australasian College of Surgeons deems this practice to be an audit, and it is mandatory for all surgeons. So why, if we want to look at outcomes in a hospital, does it become an expensive, red-tape-filled exercise in low-risk ethics? The answer is probably because of misunderstandings as to what is research vs true audit and ultimately the role of ethics committees. Another contributing factor is a desire by international journals for studies, even retrospective and really simply audit studies, to have a research and ethics approval ‘number’ prior to publication (although this is vague and may just mean ethical principles were followed). Countries such as Canada recognized this conundrum over a decade ago by having algorithm-driven ethics for low-risk endeavours that are approved immediately at no or minimal cost. The universities (and by default hospitals) offer this service as it leads to better practice and surprisingly and refreshingly, more publications. We accept without question that ethical practices must be followed and used to their full extent where appropriate, but the processes need to evolve rather than devolve to a situation discouraging analysis, reflection and ultimately better practice. An alternate view is that independent data are the only way forward; thus, some may argue that registries are the answer. Yes, registries are important in that they are independent – the Prostate Outcomes registries are an example 4, 5 – however, their scope is limited and it often takes considerable time to get results. We need further technology, resources and thoughts regarding how to move the situation so it is almost ‘real-time’. Gaps abound, but it is clinicians and not politicians or bureaucrats that should be leading this process. Zeps et al. 6 make a good point in the commentary in this edition of BJUI that ‘there is still no national or international coordinated data repository that facilitates identification of clinical variation that can be addressed through research’. Whilst this situation remains we must strive to obtain as many data as possible and to learn from them. Data are generally de-identified and very low risk, so is it really an ethical dilemma to attempt to improve clinical practice? Many would argue it is unethical not to be doing this as best practice. None declared.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,228 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,004 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».