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Record W4403334858 · doi:10.1093/hropen/hoae061

Reply: Emerging evidence of endometrial compaction in predicting ART outcomes

2024· article· en· W4403334858 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueHuman Reproduction Open · 2024
Typearticle
Languageen
FieldMedicine
TopicOvarian function and disorders
Canadian institutionsnot available
Fundersnot available
KeywordsCompactionMedicineGeologyGeotechnical engineering

Abstract

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Sir, We would like to thank Lin et al. (2024) for their interest in our recent Human Reproduction Open publication (Al-Lamee et al., 2024) and for raising several important questions which we are happy to have the opportunity to comment on. In this study, we conducted an evidence synthesis, reporting a consolidation of the existing evidence alongside an interpretation of the results, to answer the question ‘Does endometrial compaction (EC) help predict pregnancy outcomes in those undergoing assisted reproductive technologies?’. This question can be answered using a variety of approaches, and the one that we adopted is an accepted methodology. Our approach was an evidence synthesis rather than a narrower approach to answer a single question; hence, we used the well-established PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) (Page et al., 2021) and PICO (Population, Intervention, Comparison, Outcome) methods (Higgins et al., 2024), both of which are outlined within our systematic review protocol that was prospectively registered in PROSPERO (Al-Lamee et al., 2024). Whilst we recognize the limitations and confounders posed by the available evidence, which were discussed in detail within our article (Al-Lamee et al., 2024), the investigation of factors contributing to infertility, as suggested by Lin et al. (2024) was not aligned with our research question. Owing to the wide variations in the inclusion/exclusion criteria between the studies, it was not possible to stratify these further and draw meaningful conclusions. Heterogeneity, due to differences between reported pregnancy outcomes, definition of EC, method of ultrasound and cycle protocol may have accounted for the lack of translation between the clinical pregnancy rate (CPR)/ongoing pregnancy rate (OPR) and live birth rate (LBR) findings; thus, all pooled data should be viewed within this context rather than as a stand-alone point. To explore more granular details, a prospective study would be required and, as described within our study protocol, this was not our aim. The factors highlighted by Lin et al. (2024) should certainly be considered when designing and conducting a robust randomized controlled trial in the future. However, in reality, the group of study participants included in our article (Al-Lamee et al., 2024) do reflect common diverse patient cohorts seen within real-world fertility clinics and, therefore, is applicable to clinical practice. With regard to publication bias, this is an accepted issue within the scope of scientific research. To address the impact of publication bias within the scope of the analysis conducted, we recognize the importance of publication bias being both identified and reported on transparently. Whilst this can be performed with Egger’s test, as suggested by Lin et al. (2024), in our study, we opted to use a validated and well-regarded risk of bias tool, the Newcastle–Ottawa Scale (Wells et al., 2000). Additionally, a sensitivity analysis can either be performed on a single or multiple outputs; therefore, the standard approach used within our article was aligned with the good practice guidelines for an evidence synthesis (Marušić et al., 2020). As stated by Lin et al. (2024), we do acknowledge that our systematic review and meta-analysis includes both prospective and retrospective cohort studies, and this has been discussed as a limitation within our article (Al-Lamee et al., 2024). We included 7 prospective and 14 retrospective cohort studies; therefore, the suggestion from Lin et al. (2024) to perform sub-group analyses or meta-regressions based on different study designs if there are at least 10 studies in an analysis does not apply. It is important to remember that systematic reviews and meta-analyses often serve as key tools for synthesizing evidence across studies to guide clinical practice, public health interventions, or policy decisions. They can draw from different study types, including clinical trials and epidemiological studies, each offering distinct advantages and limitations (McKenzie et al., 2024). Whilst many systematic reviews focus solely on clinical trial data, this approach presents a limitation by excluding valuable epidemiological data. Finally, we thank Lin et al. (2024) for sharing their thoughts regarding other published meta-analyses looking at the association between EC and pregnancy outcomes (Chen et al., 2023; Turkgeldi et al., 2023; Feng et al., 2024). When our manuscript was initially submitted to Human Reproduction Open for review, no meta-analyses had yet been published on the topic. Since then, other systematic reviews and meta-analyses have indeed been published; however, all have included fewer studies and therefore analysed less data than ours, which may have accounted for the differences in our overall findings on CPR and OPR (Al-Lamee et al., 2024). Nevertheless, our conclusions on LBR and miscarriage rate are in keeping with other meta-analyses on the topic. Each of the three other published meta-analyses (Chen et al., 2023; Turkgeldi et al., 2023; Feng et al., 2024) looking at EC and pregnancy outcomes have used different methods, research questions, and outcomes and have included different studies. We appreciate that there are many other ways to conduct a systematic review and meta-analysis; however, each methodology used has its own merit (and, in turn, disadvantages). In summary, we have used an accepted objective method to answer our specific stated research question that, at the time of writing, includes more data than any of the other published meta-analyses. D.K.H. has received honoraria for consultancy for Theramex and has received payment for presentations from Theramex and Gideon Richter. The remaining authors have no conflicts of interest to report.

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.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.193
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.119
GPT teacher head0.405
Teacher spread0.286 · 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