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Record W2007949372 · doi:10.1093/humrep/dev079

Preimplantation genetic screening using comprehensive chromosome screening: evidence and remaining challenges

2015· letter· en· W2007949372 on OpenAlexaff
Elias M. Dahdouh, Jacques Balayla, Juan A. García-Velasco

Bibliographic record

VenueHuman Reproduction · 2015
Typeletter
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsGeneticsBiologyMedicineComputational biology

Abstract

fetched live from OpenAlex

Sir, We read with great interest a recent systematic review by Lee et al. published in your journal evaluating the clinical effectiveness of preimplantation genetic diagnosis for aneuploidy in all 24 chromosomes (PGD-A) (Lee et al., 2015). The authors conclude that, for the time being, the role for PGD-A remains uncertain regarding its clinical- and cost- effectiveness. In our opinion, a number of observations need to be addressed with regards to the implication of their findings. First, in order to ensure standard nomenclature in preimplantation genetics, it must be made clear that what the authors refer to as PGD-A is otherwise known as preimplantation genetic screening (PGS). The term ‘PGD’ should be reserved for patients carrying specific genetic disorders in which a preimplantation diagnosis for the abnormality in question is desired. Secondly, the authors reach their conclusion through a systematic review of studies with varied levels of evidence including both randomized control trials (RCTs) and observational studies. In the presence of level I evidence from RCTs, the inclusion of lower level evidence in a systematic review may induce erroneous conclusions and adversely impact clinical practice. A recent systematic review by our team (Dahdouh et al., 2015) restricted only to RCTs dealing with PGS using comprehensive chromosome screening (CCS) technology (Yang et al., 2012; Forman et al., 2013; Scott et al., 2013a) concluded that the use of PGS-CCS for the purpose of embryo selection in good-prognosis patients with normal ovarian reserve was favourable. Implantation rates (IR) were improved in the three RCTs with PGS-CCS following blastocyst biopsy. Using this approach, elective single embryo transfer (eSET) is optimized by improving the chance of delivering a healthy term singleton. With the high IR reported using PGS-CCS, eSET practice should be the standard of care. According to our review, the minimal standard for the success of this technology in today's practice should be having enough experience with embryo biopsy and extended embryo culture, and validating the genetic platforms for CCS in order not to discard normal euploid embryos. We agree that the use of PGS with fluorescence in situ hybridization (FISH) following cleavage-stage biopsy did not confer any advantageous results in IVF practice. However, in our opinion, the reason was not primarily related to the FISH technology, where up to 80% of embryonic aneuploidy can be diagnosed using 12 probes, but rather to the combination of FISH and day-3 embryo biopsy. Cleavage-stage biopsy might have been deleterious on embryo development under certain circumstances (e.g. retrieving two blastomeres, biopsy on poor quality embryos). In level I evidence data comparing day-3 to day-5 embryo biopsies (Scott et al., 2013b), cleavage-stage biopsy was associated with a 39% decrease in implantation potential, whereas no impact on embryo development was observed following blastocyst biopsy. A valid criticism regarding this paper was that IR in both day-3 and day-5 embryos were surprisingly equivalent. However, some investigators reported positive clinical outcomes from RCTs on PGS applied on day-3 embryo biopsy, both with FISH (Rubio et al., 2013) and with array comparative genomic hybridization (aCGH) (preliminary results, ClinicalTrials.gov NCT01571076). Therefore, embryo culture conditions and biopsy media and technique, are key laboratory aspects to consider for the ideal PGS practice. In addition, the authors claim that there are no reports on subsequent embryo transfer cycles following PGS-CCS. However, some authors recently reported increased IR with subsequent euploid frozen blastocyst transfers (Yang et al., 2013). With regards to cost-effectiveness, we agree with the authors that studies performed on the cost of PGS-CCS following at least two embryo transfer cycles compared with control groups will be needed to resolve this matter. Future RCTs on PGS-CCS should be conducted on a multicentre basis, including different geographic locations, different patient populations (e.g. decreased ovarian reserve, advanced maternal age) and different embryo stage biopsy (e.g. day-3 versus day-5). We are witnessing the early days of the development of this new form of PGS; robust evidence is still needed from ongoing RCTs before it is applied on a regular basis.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.018
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.068
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0030.005
Open science0.0020.002
Research integrity0.0180.018
Insufficient payload (model declined to judge)0.0040.002

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 head0.287
GPT teacher head0.364
Teacher spread0.077 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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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Citations10
Published2015
Admission routes1
Has abstractno

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