Global Consensus on the Management of Limbal Stem Cell Deficiency
Bibliographic record
Abstract
PURPOSE: In recent decades, the medical and surgical treatment of limbal stem cell deficiency (LSCD) has evolved significantly through the incorporation of innovative pharmacological strategies, surgical techniques, bioengineering, and cell therapy. With such a wide variety of options, there is a need to establish a global consensus on the preferred approaches for the medical and surgical treatment of LSCD. METHODS: An international LSCD Working Group was established by the Cornea Society in 2012 and divided into subcommittees. Four face-to-face meetings, frequent email discussions, and teleconferences were conducted since then to reach agreement on a strategic plan and methods after a comprehensive literature search. A writing group drafted the current study. RESULTS: A consensus in the medical and surgical management of LSCD was reached by the Working Group. Optimization of the ocular surface by eyelid and conjunctival reconstruction, antiinflammatory therapy, dry eye and meibomian gland dysfunction treatment, minimization of ocular surface toxicity from medications, topical medications that promote epithelialization, and use of a scleral lens is considered essential before surgical treatment of LSCD. Depending on the laterality, cause, and stage of LSCD, surgical strategies including conjunctival epitheliectomy, amniotic membrane transplantation, transplantation of limbal stem cells using different techniques and sources (allogeneic vs. autologous vs. ex vivo-cultivated), transplantation of oral mucosal epithelium, and keratoprosthesis can be performed as treatment. A stepwise flowchart for use in treatment decision-making was established. CONCLUSIONS: This global consensus provides an up-to-date and comprehensive framework for the management of LSCD.
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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 | 0.069 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".