Müller Muscle Conjunctival Resection: A Multicentered Prospective Analysis of Surgical Success: Erratum
Bibliographic record
Abstract
In the prepublished version of the article, Table 1 contained an error: The number of “Male” and “Female” participants listed was switched. The number of female participants was recorded as 46 (30.3%) and male participants, 106 (69.7%). In Table 2, alignment discrepancies were present. Both corrected tables appear below. TABLE 1. - Demographics and past ocular history of participants Characteristic N (%) Sex Female 106 (69.7%) Male 46 (30.3%) Race White/Caucasian 110 (72.4%) Asian 10 (6.6%) Hispanic and Latino 21 (13.8%) Black/African Canadian 4 (2.6%) Other 7 (4.6%) History of previous ptosis surgery Yes 9 (5.9%) No 143 (94.1%) History of previous intraocular surgery Yes 29 (19.1%) No 123 (80.9%) Mechanism of ptosis* Involutional 127 (83.6%) Congenital 6 (3.9%) Traumatic 12 (7.9%) Disease-specific Myasthenia gravis 2 (1.3%) Oculopharyngeal muscular dystrophy 2 (1.3%) Horner’s 2 (1.3%) Human immunodeficiency virus 1 (0.7%) Floppy eyelid syndrome 5 (3.3%) Contact lens wear Yes 16 (10.5%) No 136 (89.5%) Daily use of topical eye drops Yes 58 (38.2%) No 94 (61.8%) *Note that 4 patients had a combination of mechanisms underlying ptosis. TABLE 2. - Preoperative, intraoperative, and postoperative measures of the operated eyelid Mean (SD) Min Max Preoperative measures MRD1 prephenylephrine (mm) 0.76 (0.89) –2 4 MRD1 postphenylephrine (mm) 2.96 (1.07) 0.5 6 Change in MRD1 with phenylephrine (mm) 2.23 (0.98) –1.5 5.5 Levator excursion/function (mm) 14.48 (2.15) 4 20 Intraoperative measures Tissue resection length (mm) 9.43 (1.83) 6 14 Postoperative measures MRD1 3 months postoperative (mm) 2.98 (1.18) –1 5.5 MRD1, marginal reflex distance-1.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".