Notice of Retraction: Worthington MA et al. Dynamic Prediction of Outcomes for Youth at Clinical High Risk for Psychosis: A Joint Modeling Approach. <i>JAMA Psychiatry.</i> 2023;80(10):1017-1025.
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
To the Editor In consulting with another group attempting to replicate our analyses, we have identified a coding error in the joint modeling analyses in our article, "Dynamic Prediction of Outcomes for Youth at Clinical High Risk for Psychosis: A Joint Modeling Approach," 1 published online on August 2, 2023, and in the October 2023 issue of JAMA Psychiatry.Specifically, the converter by time interaction term used in the feature selection phase of the longitudinal mixed-effects analyses was mistakenly retained in the longitudinal mixed-effects component of the joint models, but only the time effect should have been used in this phase.As a result of this error, the short-term longitudinal features used to boost performance of the baseline prediction models contained information on the outcomes to be predicted, making them not about prediction per se but about describing differential change as a function of outcome.The results for the base models and the feature selection stage are correct, but the results for the joint models that combine the base models and the selected longitudinal features are subject to this error.When the joint model analyses were rerun with the correct coding, they no longer showed improved prediction accuracy over and above the performance of the Cox regression models incorporating baseline-only predictors.Because the primary significance and novelty of the article were based on improved prediction in joint models incorporating information on short-term clinical change, we have requested that the article be retracted.Until studies of new samples are completed, short-term (baseline to 2-month) clinical change cannot be used to boost the performance of baseline-only prediction models of psychosis and remission of clinical high-risk status.We apologize for any confusion this error may have caused.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Research integrity Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.010 | 0.119 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".