Retinal haemorrhage description tool
Bibliographic record
Abstract
BACKGROUND: Retinal haemorrhages are an important finding in children with abusive and accidental head trauma. There are no standardised and validated protocols to describe them in a consistent manner. The aim of this web-based study was to establish the reliability and validity of a new retinal haemorrhage description tool. MATERIALS AND METHOD: Our tool is a comprehensive questionnaire, which is applied using a retinal schematic that divides the retina into four independent zones. Four independent observers scored retinal haemorrhages from 80 retinal photographs. Inter- and intra-rater agreement (by repeat assessment of 10 photographs for each examiner) were calculated using Fleiss κ statistics. RESULTS: A high inter-rater agreement was noted for haemorrhages in the peripapillary zones, whereas agreement was only fair for all other zones. Intra-rater agreement was high only for the posterior pole. Photographs may be an unreliable way of documenting retinal haemorrhages particularly from the peripheral retina, thus underscoring the importance of a thorough clinical examination. CONCLUSION: This study shows that the tool achieves some validity for describing haemorrhages in the posterior retina. It performs less well in the peripheral zones.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".