Corrigendum. ICSI does not improve reproductive outcomes in autologous ovarian response cycles with non-male factor subfertility
Bibliographic record
Abstract
Hum Reprod 2020:35: pp. 583–594 The authors would like to apologise for the errors in Table 1, Table 3 and the manuscript of the above article. The errors, unfortunately, occurred in the process of transcribing the data across three platforms (Microsoft Excel, SPPS and then on to Microsoft Word). This has now been rechecked by three different individuals to avoid further transcription errors. Amendments have also been made to Table 4 to further clarify its intention. Distribution of IVF and ICSI cycles in poor ovarian response according to female age groups, number of previous ART cycles, number of previous live birth(s) through ART, oocyte yield, stage of transfer, number of embryos transferred and a sub-analysis of all ovarian response categories. 1466 31742 18556 (55%) 1357 591 (2%) 0.107 0.504 62641 33436 (12%) 29205 (10%) 15892 8067 (33%) 7825 (34%) 1466 31742 18556 (55%) 1357 591 (2%) 0.107 0.504 62641 33436 (12%) 29205 (10%) 15892 8067 (33%) 7825 (34%) Multivariable logistic regression–adjusted for female age, number of previous ART cycles, number of previous live births through ART, oocyte yield. The data is expressed as whole numbers and percentages. The adjusted Odds Ratio (aOR) is displayed as the odds of performing ICSI over IVF for each of the variables. Distribution of IVF and ICSI cycles in poor ovarian response according to female age groups, number of previous ART cycles, number of previous live birth(s) through ART, oocyte yield, stage of transfer, number of embryos transferred and a sub-analysis of all ovarian response categories. 1466 31742 18556 (55%) 1357 591 (2%) 0.107 0.504 62641 33436 (12%) 29205 (10%) 15892 8067 (33%) 7825 (34%) 1466 31742 18556 (55%) 1357 591 (2%) 0.107 0.504 62641 33436 (12%) 29205 (10%) 15892 8067 (33%) 7825 (34%) Multivariable logistic regression–adjusted for female age, number of previous ART cycles, number of previous live births through ART, oocyte yield. The data is expressed as whole numbers and percentages. The adjusted Odds Ratio (aOR) is displayed as the odds of performing ICSI over IVF for each of the variables. Clinical pregnancy, live birth, singleton and multiple birth rate per treatment cycle in all ovarian response for IVF and ICSI cycles distributed by the variables female age, number of previous ART cycles, number of previous live births through ART, oocyte yield and stage of embryo transfer expressed in whole numbers and as a percentage. 17352 (9.4) 57704 (39.9) 50692 (35.1) 34905 (24.2) 15787 (10.9) 4947 (14.8) 17352 (9.4) 57704 (39.9) 50692 (35.1) 34905 (24.2) 15787 (10.9) 4947 (14.8) Clinical pregnancy, live birth, singleton and multiple birth rate per treatment cycle in all ovarian response for IVF and ICSI cycles distributed by the variables female age, number of previous ART cycles, number of previous live births through ART, oocyte yield and stage of embryo transfer expressed in whole numbers and as a percentage. 17352 (9.4) 57704 (39.9) 50692 (35.1) 34905 (24.2) 15787 (10.9) 4947 (14.8) 17352 (9.4) 57704 (39.9) 50692 (35.1) 34905 (24.2) 15787 (10.9) 4947 (14.8) The odds ratio of IVF vs ICSI for clinical pregnancy and live birth outcome subcategorised by 5-year intervals from 2002 up to 2016 for poor ovarian response cohort and by oocyte yield for 1998 to 2016, with 99.5% and 95% confidence intervals and p-values respectively. IVF (n=11,651) ICSI (n=11,362) 1.04 (0.93 – 1.16) 1 0.354 1.03 (0.91 – 1.16) 1 0.500 IVF (n=10,743) ICSI (n=9,836) 1.13 (0.99 – 1.28) 1 0.010 1.14 (0.99 – 1.30) 1 0.006 IVF (n=7,293) ICSI (n=5,837) 1.04 (0.89 – 1.22) 1 0.473 1.04 (0.87 – 1.23) 1 0.573 IVF (n=33,436) ICSI (n=29,205) 1.04 (0.97 – 1.12) 1 0.102 1.03 (0.96 – 1.11) 1 0.261 IVF (n=115,639) ICSI (n=123,497) 1.02 (0.99 – 1.05) 1 0.014 1.03 (0.99 – 1.05) 1 0.012 IVF (n=78,504) ICSI (n=91,284) 0.99 (0.96 – 1.02) 1 0.314 0.99 (0.96 – 1.02) 1 0.261 IVF (n=27,829) ICSI (n=33,452) 0.97 (0.92 – 1.02) 1 0.048 0.98 (0.93 – 1.03) 1 0.286 IVF (n=10,750) ICSI (n=12,736) 0.97 (0.90 – 1.06) 1 0.368 0.98 (0.90 – 1.07) 1 0.480 IVF (n=6,275) ICSI (n=6,998) 0.88 (0.77 – 1.00) 1 0.005 0.88 (0.77 – 1.01) 1 0.011 *aOR (95% CI) Clinical Pregnancy *aOR (95% CI) Live Birth IVF (n=18,556) ICSI (n=13,186) 1.05 (0.96 – 1.16) 1 0.126 1.03 (0.93 – 1.14) 1 0.451 IVF (n=66,863) ICSI (n=58,320) 1.04 (1.00 – 1.07) 1 0.005 1.04 (1.00 – 1.08) 1 0.005 IVF (n=47,008) ICSI (n=44,926) 0.99 (0.96 – 1.03) 1 0.671 1.00 (0.96 – 1.04) 1 0.968 IVF (n=17,386) ICSI (n=17,252) 0.97 (0.91 – 1.04) 1 0.187 1.00 (0.93 – 1.07) 1 0.997 IVF (n=6,967) ICSI (n=6,882) 1.02 (0.92 – 1.14) 1 0.530 1.02 (0.92 – 1.14) 1 0.548 IVF (n=4,270) ICSI (n=4,084) 0.91 (0.77 – 1.07) 1 0.109 0.92 (0.78 – 1.09) 1 0.156 IVF (n=11,651) ICSI (n=11,362) 1.04 (0.93 – 1.16) 1 0.354 1.03 (0.91 – 1.16) 1 0.500 IVF (n=10,743) ICSI (n=9,836) 1.13 (0.99 – 1.28) 1 0.010 1.14 (0.99 – 1.30) 1 0.006 IVF (n=7,293) ICSI (n=5,837) 1.04 (0.89 – 1.22) 1 0.473 1.04 (0.87 – 1.23) 1 0.573 IVF (n=33,436) ICSI (n=29,205) 1.04 (0.97 – 1.12) 1 0.102 1.03 (0.96 – 1.11) 1 0.261 IVF (n=115,639) ICSI (n=123,497) 1.02 (0.99 – 1.05) 1 0.014 1.03 (0.99 – 1.05) 1 0.012 IVF (n=78,504) ICSI (n=91,284) 0.99 (0.96 – 1.02) 1 0.314 0.99 (0.96 – 1.02) 1 0.261 IVF (n=27,829) ICSI (n=33,452) 0.97 (0.92 – 1.02) 1 0.048 0.98 (0.93 – 1.03) 1 0.286 IVF (n=10,750) ICSI (n=12,736) 0.97 (0.90 – 1.06) 1 0.368 0.98 (0.90 – 1.07) 1 0.480 IVF (n=6,275) ICSI (n=6,998) 0.88 (0.77 – 1.00) 1 0.005 0.88 (0.77 – 1.01) 1 0.011 *aOR (95% CI) Clinical Pregnancy *aOR (95% CI) Live Birth IVF (n=18,556) ICSI (n=13,186) 1.05 (0.96 – 1.16) 1 0.126 1.03 (0.93 – 1.14) 1 0.451 IVF (n=66,863) ICSI (n=58,320) 1.04 (1.00 – 1.07) 1 0.005 1.04 (1.00 – 1.08) 1 0.005 IVF (n=47,008) ICSI (n=44,926) 0.99 (0.96 – 1.03) 1 0.671 1.00 (0.96 – 1.04) 1 0.968 IVF (n=17,386) ICSI (n=17,252) 0.97 (0.91 – 1.04) 1 0.187 1.00 (0.93 – 1.07) 1 0.997 IVF (n=6,967) ICSI (n=6,882) 1.02 (0.92 – 1.14) 1 0.530 1.02 (0.92 – 1.14) 1 0.548 IVF (n=4,270) ICSI (n=4,084) 0.91 (0.77 – 1.07) 1 0.109 0.92 (0.78 – 1.09) 1 0.156 Clinical pregnancy and live birth outcome adjusted for female age, number of previous ART cycles, number of previous live birth(s) through ART, oocyte yield, stage of transfer, method of fertilisation and number of embryos transferred. Clinical pregnancy and live birth outcome adjusted for female age, oocyte yield, stage of transfer, method of fertilisation and number of embryos transferred. The odds ratio of IVF vs ICSI for clinical pregnancy and live birth outcome subcategorised by 5-year intervals from 2002 up to 2016 for poor ovarian response cohort and by oocyte yield for 1998 to 2016, with 99.5% and 95% confidence intervals and p-values respectively. IVF (n=11,651) ICSI (n=11,362) 1.04 (0.93 – 1.16) 1 0.354 1.03 (0.91 – 1.16) 1 0.500 IVF (n=10,743) ICSI (n=9,836) 1.13 (0.99 – 1.28) 1 0.010 1.14 (0.99 – 1.30) 1 0.006 IVF (n=7,293) ICSI (n=5,837) 1.04 (0.89 – 1.22) 1 0.473 1.04 (0.87 – 1.23) 1 0.573 IVF (n=33,436) ICSI (n=29,205) 1.04 (0.97 – 1.12) 1 0.102 1.03 (0.96 – 1.11) 1 0.261 IVF (n=115,639) ICSI (n=123,497) 1.02 (0.99 – 1.05) 1 0.014 1.03 (0.99 – 1.05) 1 0.012 IVF (n=78,504) ICSI (n=91,284) 0.99 (0.96 – 1.02) 1 0.314 0.99 (0.96 – 1.02) 1 0.261 IVF (n=27,829) ICSI (n=33,452) 0.97 (0.92 – 1.02) 1 0.048 0.98 (0.93 – 1.03) 1 0.286 IVF (n=10,750) ICSI (n=12,736) 0.97 (0.90 – 1.06) 1 0.368 0.98 (0.90 – 1.07) 1 0.480 IVF (n=6,275) ICSI (n=6,998) 0.88 (0.77 – 1.00) 1 0.005 0.88 (0.77 – 1.01) 1 0.011 *aOR (95% CI) Clinical Pregnancy *aOR (95% CI) Live Birth IVF (n=18,556) ICSI (n=13,186) 1.05 (0.96 – 1.16) 1 0.126 1.03 (0.93 – 1.14) 1 0.451 IVF (n=66,863) ICSI (n=58,320) 1.04 (1.00 – 1.07) 1 0.005 1.04 (1.00 – 1.08) 1 0.005 IVF (n=47,008) ICSI (n=44,926) 0.99 (0.96 – 1.03) 1 0.671 1.00 (0.96 – 1.04) 1 0.968 IVF (n=17,386) ICSI (n=17,252) 0.97 (0.91 – 1.04) 1 0.187 1.00 (0.93 – 1.07) 1 0.997 IVF (n=6,967) ICSI (n=6,882) 1.02 (0.92 – 1.14) 1 0.530 1.02 (0.92 – 1.14) 1 0.548 IVF (n=4,270) ICSI (n=4,084) 0.91 (0.77 – 1.07) 1 0.109 0.92 (0.78 – 1.09) 1 0.156 IVF (n=11,651) ICSI (n=11,362) 1.04 (0.93 – 1.16) 1 0.354 1.03 (0.91 – 1.16) 1 0.500 IVF (n=10,743) ICSI (n=9,836) 1.13 (0.99 – 1.28) 1 0.010 1.14 (0.99 – 1.30) 1 0.006 IVF (n=7,293) ICSI (n=5,837) 1.04 (0.89 – 1.22) 1 0.473 1.04 (0.87 – 1.23) 1 0.573 IVF (n=33,436) ICSI (n=29,205) 1.04 (0.97 – 1.12) 1 0.102 1.03 (0.96 – 1.11) 1 0.261 IVF (n=115,639) ICSI (n=123,497) 1.02 (0.99 – 1.05) 1 0.014 1.03 (0.99 – 1.05) 1 0.012 IVF (n=78,504) ICSI (n=91,284) 0.99 (0.96 – 1.02) 1 0.314 0.99 (0.96 – 1.02) 1 0.261 IVF (n=27,829) ICSI (n=33,452) 0.97 (0.92 – 1.02) 1 0.048 0.98 (0.93 – 1.03) 1 0.286 IVF (n=10,750) ICSI (n=12,736) 0.97 (0.90 – 1.06) 1 0.368 0.98 (0.90 – 1.07) 1 0.480 IVF (n=6,275) ICSI (n=6,998) 0.88 (0.77 – 1.00) 1 0.005 0.88 (0.77 – 1.01) 1 0.011 *aOR (95% CI) Clinical Pregnancy *aOR (95% CI) Live Birth IVF (n=18,556) ICSI (n=13,186) 1.05 (0.96 – 1.16) 1 0.126 1.03 (0.93 – 1.14) 1 0.451 IVF (n=66,863) ICSI (n=58,320) 1.04 (1.00 – 1.07) 1 0.005 1.04 (1.00 – 1.08) 1 0.005 IVF (n=47,008) ICSI (n=44,926) 0.99 (0.96 – 1.03) 1 0.671 1.00 (0.96 – 1.04) 1 0.968 IVF (n=17,386) ICSI (n=17,252) 0.97 (0.91 – 1.04) 1 0.187 1.00 (0.93 – 1.07) 1 0.997 IVF (n=6,967) ICSI (n=6,882) 1.02 (0.92 – 1.14) 1 0.530 1.02 (0.92 – 1.14) 1 0.548 IVF (n=4,270) ICSI (n=4,084) 0.91 (0.77 – 1.07) 1 0.109 0.92 (0.78 – 1.09) 1 0.156 Clinical pregnancy and live birth outcome adjusted for female age, number of previous ART cycles, number of previous live birth(s) through ART, oocyte yield, stage of transfer, method of fertilisation and number of embryos transferred. Clinical pregnancy and live birth outcome adjusted for female age, oocyte yield, stage of transfer, method of fertilisation and number of embryos transferred. We would like to emphasise that the transcription errors had no impact on the message of the publication and its academic value. Whilst the ‘n’ numbers were affected, the odds ratio and confidence interval calculations were not, thus preserving the overall scientific impact of the findings from the study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.059 | 0.037 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".