Establishing Reliability When Multiple Examiners Evaluate a Single Case-Part II: Applications to Symptoms of Post-Traumatic Stress Disorder (PTSD)
Bibliographic record
Abstract
In an earlier article, the authors assessed the clinical significance of each of 19 Clinician Administered PTSD Scale items and composite scores (CAPS-1) [1] when 12 clinicians evaluated a Vietnam era veteran. A second patient was also evaluated by the same 12 clinicians and used for cross-validation purposes [2]. The objectives of this follow-up research are: (1) to describe and apply novel bio-statistical methods for establishing the statistical significance of these reliability estimates when the same 12 examiners evaluated each of the two Vietnam era patients. This approach is also utilized within the broader contexts of the ideographic and nomothetic conceptualizations to science, and the interplay between statistical and clinical or practical significance; (2) to detail the steps for applying the new methodology; and (3) to investigate whether the quality of the symptoms (frequency, intensity); item content; or specific clinician affect the levels of rater reliability. The more typical (nomothetic) reliability research design focuses on group averages and broader principles related to biomedical issues, rather than the focus on the individual case (ideographic approach). Both research designs (ideographic and nomothetic) have been incorporated in this follow-up research endeavor.
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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 | 0.185 | 0.352 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".