Estimation of equable scale scores and treatment outcomes from patient- and clinician-reported PTSD measures using item response theory calibration.
Bibliographic record
Abstract
Across multiple RCTs, discrepancies between patient and clinician reports of PTSD symptoms are at least a partial contributing factor to large discrepancies between treatment outcome effect sizes from self-report and clinician reports within the same patients. Using secondary data from the NIDA-funded Women and Trauma Study, we demonstrated Common Persons Item Response Theory (IRT) Calibration for calibrating self-reported and clinician-reported PTSD severity scores in a manner similar to the process used to produce equated scores across multiple forms of standardized tests (e.g., SAT, GRE). Under IRT calibration, treatment effect sizes between the CAPS and MPSS-SR did not differ, while with the noncalibrated measures, the CAPS effect size was 85% larger than the MPSS-SR. Further, across the range of a combined CAPS/MPSS-SR gold standard, IRT-calibrated CAPS and MPSS-SR individual scores did not differ; for uncalibrated individual scores, MPSS scores were higher than CAPS scores at higher levels of PTSD severity while the reverse was true at lower levels of severity. The use of IRT calibration approaches for calibrating self-report and clinical interview measures of PTSD will allow treatment researchers to reflect the treatment effect on PTSD as a construct (regardless of reporter) as opposed to being limited to reporting treatment effects that may be discrepant within patients and specific to the particular assessment measure being employed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.344 | 0.580 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".