Digit ratios (2D:4D) determined by computer‐assisted analysis are more reliable than those using physical measurements, photocopies, and printed scans
Bibliographic record
Abstract
Prenatal androgens influence the second to fourth digit ratio (2D:4D) of hands with men having lower ratios than women. Numerous methods are used to assess 2D:4D including, physical measurements with calipers, and measurements made from photocopies, scanned images, digital photographs, radiographs, and scaled tubes. Although each method appears relatively reliable, agreement upon a gold standard is necessary to better explore the putative effects of prenatal androgens. Our objective was to assess the level of intra and interobserver reliability when evaluating 2D:4D using four techniques: (1) physical measurements, (2) photocopies, (3) printed scanned images, and (4) computer-assisted image analysis. Physical measurements, photocopies, and printed scanned images were measured with Vernier calipers. Scanned images were also measured with computer-based calipers. Measurements were made in 30 men and 30 women at two different time points, by three experienced observers. Intraclass correlation coefficients were used to assess the level of reliability. Intraobserver reliability was best for computer-assisted (0.957), followed by photocopies (0.939), physical measurements (0.925), and printed scans (0.842; P = 0.015). Interobserver reliability was also greatest for computer-assisted (0.892), followed by photocopies (0.858), physical measurements (0.795), and printed scans (0.761; P = 0.001). Mean 2D:4D from physical measurements were higher than all other techniques (P < 0.0001). Digit ratios determined from computer-assisted, physical measurements, and printed scans were more reliable in men than women (P = 0.009, P = 0.017, and P = 0.012, respectively). In summary, 2D:4D determined from computer-assisted analysis yielded the most accurate and consistent measurements among observers. Investigations of 2D:4D should use computer-assisted measurements over alternate methods whenever possible.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".