Congenital limb deficiency classification and nomenclature: The need for a consensus
Bibliographic record
Abstract
After the thalidomide epidemic in the early 1960s, many jurisdictions developed congenital anomaly surveillance systems. Congenital limb deficiencies can act as indicators of potential teratogens. The classification of congenital limb deficiencies is essential to determine the precise cause or causes of this anomaly. This article describes the different terminology and classification that have been used over time and the need for a consensus. While there are a variety of studies examining the epidemiology and etiology of congenital limb deficiencies, there is an inconsistent use of terminology and classification which makes comparisons between studies challenging. © 2016 Wiley Periodicals, Inc.
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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 | 0.046 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.011 | 0.027 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".