Right or wrong? Looking through the retrospectoscope to analyse predictions made a decade ago in prenatal diagnosis and fetal surgery
Bibliographic record
Abstract
Usually, around mid-October, the Associate Editors of Prenatal Diagnosis meet to make plans for the year to come and reflect on the year just gone. 2] This year, however, was different in more ways than one! COVID-19 meant we could not travel and so, instead of meeting in person in North America, we met by Zoom for two, 3-h sessions over a weekendat 07.00 in the USA and Toronto, 22.00 in Melbourne, 13.00 in Belgium and midday in the UK (Figure Not nearly so much fun, but none-the-less productive. The other difference is that this is Prenatal Diagnosis's 40th anniversary, and we chose to reflect on the predictions made 10 years ago in our 30th Anniversary issue, 8-12 as COVID-related issues have eclipsed many advances made this year and are discussed elsewhere in this issue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".