Preoperative Variables Associated with Surgical Outcome for the Correction of Exodeviation
Bibliographic record
Abstract
The success rate of exodeviation surgery in existing literature has been shown to be variable. This study sought to determine the success rate of surgery for exodeviation in Atlantic Canada and determine variables associated with surgical outcome. A retrospective chart review was performed, considering patients who had been assessed and surgically treated for exodeviation at the IWK Health Centre between 2011-2018. This study included 176 subjects, aged 1-75 years. Preoperative variables were compared between subjects with successful versus unsuccessful surgical outcomes, using the chi square, Fischer's exact test and binary logistic regression. A success rate of 43% was determined. Smaller preoperative deviation size at near and distance fixation, as well as the basic type classification were associated with successful operative outcome. Left eye acuity showed a statistically significant association with surgical success outcome. In conclusion, these findings compliment those of previous groups, suggesting exodeviation surgery outcome is variable. Our results add to a growing list of variables implicated in outcomes for these subjects. A smaller deviation preoperatively was associated with success in existing data and in this study, and these findings may suggest a potential role for basic subtype into future exodeviation literature.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".