The Role of Follow-up Evaluation in the Diagnostic Algorithm of Idiopathic Interstitial Pneumonia: A Retrospective Study
Bibliographic record
Abstract
Abstract We aimed to evaluate the alteration of diagnosis of individual expert and multidisciplinary discussion (MDD) team in the longitudinal diagnostic assessment of idiopathic interstitial pneumonia (IIP). The retrospective analysis included 56 patients diagnosed as IIP by The First Affiliated Hospital of Guangzhou Medical University with follow-up visits during Jan 1st to Aug 31st 2014. Each expert was provided information in a sequential manner and was asked to assign an individual diagnosis and an MDD diagnosis after group discussion. The level of agreement among individual experts and between different visits was calculated by kappa and the agreement between individual specialist and MDD team with different consensus levels was measured by weighted-kappa coefficients. Follow-up data changed the original clinical diagnosis and MDD diagnosis in 24.1% and 10.7% of all cases, respectively, and clinician and MDD consensus level in 55.4% and 25.0%, respectively. The diagnostic performance of individual clinicians or radiologist was closer to that of the MDD compared with the pathologist, and follow-up further increased the agreement. The longitudinal evaluation of patients with IIP improved the inter-observer agreement in a multidisciplinary team. The performance of individual clinicians or radiologist was approaching the accuracy of multidisciplinary team when provided with follow-up data.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".