The use of optical coherence tomography (<scp>OCT</scp>) and <scp>OCT</scp> angiography in borderline personality disorder compared to health control subjects
Bibliographic record
Abstract
BACKGROUND: Optical coherence tomography (OCT) or OCT angiography (OCTA) has been investigated in few research studies of psychiatric disorders. No research has been done using OCT or OCTA in patients with borderline personality disorder (BPD). METHODS: OCTA measured foveal avascular zone (FAZ), macular vessel density (MVD), and peripapillary vessel density (PVD). OCT measured the peripapillary retinal fiber layer (RNFL) and central retinal thickness (CRT). The study utilized the Ottawa Self-Injury Inventory, Hamilton Anxiety Rating Scale (HAMA), and Global Assessment of Functioning (GAF) to assess the symptom characteristics of individuals with BPD. RESULTS: Fifty-nine eyes of BPD patients and 58 eyes of normal subjects were analyzed, MVD of the superficial retinal capillary plexus declined noticeably in most subfields (p < 0.05). Significant differences were observed in the whole inner ring and outer ring index between BPD and HC groups (p < 0.05). The patients with BPD exhibited lower RNFL and CRT, the difference was significant (p < 0.05). CRT indicated a significant negative correlation with the Ottawa Self-Injury Inventory (p < 0.05). In addition, we observed that there was a negative correlation identified between the MVD of the inner ring and HAMA (p < 0.05). Meanwhile, the MVD of the outer ring was positively correlated with GAF (p < 0.05). The areas under the receiver operating characteristic curves (AUROCs) for distinguishing BPD and HC eyes in OCTA were the highest for fovea MVD (0.679), followed by outer ring MVD (0.669), inner ring MVD (0.641), FAZ (0.579). In OCT, CRT was highest for BPD (0.711), followed by RNFL (0.625). CONCLUSION: The OCT and OCTA can non-invasively detect microvascular and morphology changes of the retina in BPD patients compared to healthy control subjects.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".