Doing more than Just Acknowledging Attrition at Follow-Up: A Comment on Lu, Cheng, and Chen (2013)
Bibliographic record
Abstract
Lu, Cheng, and Chen (2013 ) faced one of the most common challenges encountered in longitudinal studies: follow-up attrition. Using a correlational prospective design, 464 volunteers completed a questionnaire that measured the constructs of the theory of planned behavior, and subsequently 154 of them provided physical activity data at a 6-month follow-up. The proportion of participants (66.8%) for whom the investigators were not able to gather information on the behavioral outcome at follow-up may reflect a form of selection bias that may affect both the validity and generalizability of study results. Lu, et al.'s (2013 ) study is used here to explore the implication of follow-up attrition on the results and inference, to review what information should be reported in a scientific paper in such situations, and to give practical tips to handle follow-up attrition.
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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 | 0.051 | 0.217 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.012 | 0.006 |
| Research integrity | 0.050 | 0.083 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".