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Record W4403018285 · doi:10.52054/fvvo.16.3.042

Achieving successful outcomes with endometrial ablation needs better case selection

2024· article· en· W4403018285 on OpenAlexaboutno aff
T. Justin Clark

Bibliographic record

VenueFacts Views and Vision in ObGyn · 2024
Typearticle
Languageen
FieldMedicine
TopicUterine Myomas and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsEndometrial ablationSelection (genetic algorithm)AblationCase selectionComputer scienceMedicineInternal medicineSurgeryArtificial intelligence

Abstract

fetched live from OpenAlex

When uterine sparing techniques that destroyed the endometrium were introduced in the 1990s, the demise of hysterectomy for heavy menstrual bleeding seemed probable with the introduction of this effective, less invasive alternative method of surgery (O'Connor et al., 1997).With the introduction of rapid, semiautomated ways of cooking the endometrium, surgical proficiency in hysteroscopy became surplus to requirements as did the absolute necessity for an anaesthetist (Clark et al., 2011).However, endometrial ablation, as with pretty much all new health care innovations, is now seeing the initial euphoria that greeted its arrival on the health care scene, tempered by a healthy scepticism as longer-term prognostic data are inexorably accumulated.Hysterectomy has not disappeared and in fact remains in rude health, especially with the popularity of laparoscopic approaches and day-case models of care (Antoun et al., 2021).Moreover, it has long been known that a substantial proportion of women will have their uterus removed following the index ablative procedure.Recent reviews of the evidence base suggest the rates are around 12% within 5 years of this 'uterine sparing' ablative procedure (Oderkerk et al., 2023).In this issue of Facts, Views and Vision, McGee and colleagues (McGee et al., 2024) present the largest and longest longitudinal prognostic cohort of patients having undergone some form of endometrial destruction.The authors should he congratulated for their endeavour, interrogating a variety of large electronic datasets in Ontario, Canada and tracking whether these patients required subsequent uterine surgery and in particular hysterectomy.Their findings are generally in keeping with those of previous cohorts (Bansi-Matharu et al., 2013;Oderkerk et al., 2023), with 16% of women having a hysterectomy at five years, 23% at 10 years and 29% at 15 years.Whilst the rate of hysterectomy slows over time, this evaluation does not show any 'plateau' effect, where "treatment failures" no longer exist.However, is it rational to use hysterectomy as a surrogate for failure of endometrial ablative treatment?The indication for subsequent surgery could not be extracted from the routinely collected healthcare databases by the authors of the current paper.Despite this deficiency, it seems reasonable to assume that hysterectomy within two, and possibly five years, is most likely due to ongoing uterine symptoms, such as bleeding or pain.However, is it fair to assume this when judged more than five, and especially more than 10, years later because the indication for hysterectomy may very well not relate to menstrual bleeding and / or pain?If most re-interventions are indicated for ongoing or new bleeding symptoms and / or pain then the concomitant use of levonorgesterol-releasing intrauterine systems (LNG-IUS) (Oderkerk et al., 2021), as highlighted by the authors in their write up, may help reduce subsequent hysterectomy for these indications.The MIRA2 trial, randomising women to endometrial ablation with or without LNG-IUS, has recently completed recruitment and we await these results with interest to see if this synergy can improve the outcomes following endometrial ablation (Oderkerk et al., 2022).Interrogation of large, routinely collected health datasets delivers precision around outcomes but such evaluations lack granularity.This is because these 'big data' resources do not generally collect detailed additional demographic and clinical information that may aid our understanding.For example, clinical data such as pain, pre-existing gynaecological diagnoses like endometriosis, and ultrasonic data of structural uterine pathologies such as adenomyosis and fibroids, would allow an analysis of the effect on prognosis of these variables.Furthermore, all forms of endometrial destruction whether they were first Achieving successful outcomes with endometrial ablation needs better case selection

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.346
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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