Where Should We Intervene, 20 Years Later? Case–Control and Prospective Cohort Designs Provide Similar Answers
Bibliographic record
Abstract
In 2000, this journal published an influential case–control study identifying dynamic risk factors for sexual recidivism (Hanson & Harris, 2000). In 2017, updated recidivism information for the same sample was obtained with an average follow-up of 20 years. The current study compared the risk factors that differentiated between sexual recidivists and nonrecidivists between the two research designs: original case–control and updated prospective cohort. Of the 82 comparisons, 50 favored the prospective design while 32 favored the case–control; however, most of the differences were small and nonsignificant. Static and dynamic risk factors were approximately equivalent between study designs. Factors identified as sex-specific (e.g., sexual deviancy) were also equivalent between designs while general risk factors (e.g., substance use) were more likely identified in the prospective design. Overall, case–control studies can be used for the identification of risk factors, especially for low base rate behaviors such as sexual recidivism.
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 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 | 0.090 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.018 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.017 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".