THE MINI-STEAMROLL
Bibliographic record
Abstract
PURPOSE: To describe a novel positioning maneuver for patients with rhegmatogenous retinal detachment (RRD) following pneumatic retinopexy(PnR). METHODS: Single-center prospective case series of primary RRDs referred to St. Michael's Hospital, Toronto, Canada, between 2021 and 2023. All patients underwent PnR. Baseline ultra-widefield fundus imaging and repeat imaging 10 minutes after the gas injection was performed. After PnR, patients were instructed to perform the mini-steamroll maneuver which consists of a face-down position for ten minutes followed by positioning to the retinal break. The reduction of subretinal fluid (SRF) volume after the initial face-down position was evaluated with clinical examination and ultra-widefield imaging. RESULTS: Six patients who presented with primary bullous RRD and a sizable superior break were enrolled. The mini-steamroll maneuver resulted in a rapid and significant reduction of SRF in all patients with bullous RRD and large superior breaks, allowing subretinal fluid to be expressed into the vitreous cavity with 10 minutes of face-down positioning. One patient required a sequential PnR. Primary retinal reattachment was achieved in all cases .This approach was well-tolerated by patients. CONCLUSION: This case series demonstrates that the mini-steamroll maneuver may be a suitable alternative for patient positioning following PnR in certain cases. The mini-steamroll is a simpler positioning regimen with the potential benefits of direct-to-break and full steamroller maneuver.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".