APOSTEL 2.0 Recommendations for Reporting Quantitative Optical Coherence Tomography Studies
Bibliographic record
Abstract
OBJECTIVE: To update the consensus recommendations for reporting of quantitative optical coherence tomography (OCT) study results, thus revising the previously published Advised Protocol for OCT Study Terminology and Elements (APOSTEL) recommendations. METHODS: To identify studies reporting quantitative OCT results, we performed a PubMed search for the terms "quantitative" and "optical coherence tomography" from 2015 to 2017. Corresponding authors of the identified publications were invited to provide feedback on the initial APOSTEL recommendations via online surveys following the principle of a modified Delphi method. The results were evaluated and discussed by a panel of experts and changes to the initial recommendations were proposed. A final survey was recirculated among the corresponding authors to obtain a majority vote on the proposed changes. RESULTS: A total of 116 authors participated in the surveys, resulting in 15 suggestions, of which 12 were finally accepted and incorporated into an updated 9-point checklist. We harmonized the nomenclature of the outer retinal layers, added the exact area of measurement to the description of volume scans, and suggested reporting device-specific features. We advised to address potential bias in manual segmentation or manual correction of segmentation errors. References to specific reporting guidelines and room light conditions were removed. The participants' consensus with the recommendations increased from 80% for the previous APOSTEL version to greater than 90%. CONCLUSIONS: The modified Delphi method resulted in an expert-led guideline (evidence Class III; Grading of Recommendations, Assessment, Development and Evaluations [GRADE] criteria) concerning study protocol, acquisition device, acquisition settings, scanning protocol, funduscopic imaging, postacquisition data selection, postacquisition analysis, nomenclature and abbreviations, and statistical approach. It will be essential to update these recommendations to new research and practices regularly.
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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 | 0.347 | 0.568 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.017 |
| Bibliometrics | 0.029 | 0.012 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.013 | 0.013 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".