On the Relationship between the Reliability and Accuracy of Bio-Behavioral Diagnoses: Simple Math to the Rescue
Bibliographic record
Abstract
An equivalence between the J statistic (Jack Youden, 1950) and the Kappa statistic (K), Cohen (1960), was discovered by Helena Kraemer (1982). J is defined as: [Sensitivity (Se) + Specificity (Sp)] - 1. The author (2011) added the remaining two validity components to the J Index, namely, Predicted Positive Accuracy (PPA) and Predicted Negative Accuracy (PNA). The resulting D Index or D = [(Se + Sp) + (PPA + PNA) - 1] / 2. The purpose of this research is to compare J and D as estimates of K, using both actual and simulated data sets. The actual data consisted of ratings of clinical depression and self-reports of gonorrhea. The simulated data sets represented binary diagnoses when the percentages of Negative and Positive cases were: (Identical; Slightly varying; Mildly varying; Moderately varying; or Markedly varying diagnostic patterns, For both the diagnosis of clinical depression, and the self-reports of gonorrhea, D produced closer approximations to Kappa. For the simulated data, under both identical and slightly different patterns of assigning Negative and Positive binary diagnoses, K, D and J produced identical results. While J produced acceptably close values to K under the condition of Mild discrepancies in the proportions of Negative and Positive cases, D continued to more closely approximate K. While D more closely estimated K under Markedly varying diagnostic patterns, D produced values under this extreme condition that were closer than would have been predicted. The significance of these findings for future research is discussed.
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 | 0.050 | 0.347 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 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".