Bibliographic record
Abstract
The conversion disorder that appears like syncope is common but poorly recognized. The study aimed to develop and validate a brief, clinician-administered screening tool to discriminate psychogenic nonsyncopal collapse (PNSC) among young patients referred for fainting. Consecutive patients with PNSC and with syncope (15.4 ± 2.2 years) completed a 92-item inventory highlighting features typical of PNSC and neurally mediated syncope (n = 35, each cohort). Fourteen items were retained and revised and then administered to new cohorts ultimately diagnosed with PNSC or syncope (n = 40, each cohort). Further revision led to a 10-item Fainting Assessment Inventory (FAI-10). Scoring the syncope ratings positively and the PNSC ratings negatively, median scores differed between cohorts with PNSC and with syncope (-6 vs. 7; p < 0.001). Diagnostic sensitivity (0.95), specificity (0.875), positive predictive value (0.889), negative predictive value (0.93), and area under the curve (0.973) were calculated. The FAI-10 furthers clinicians' ability to distinguish various forms of transient loss of consciousness.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".