Acupuncture treatment for optic atrophy
Bibliographic record
Abstract
BACKGROUND: Optic atrophy (OPA) is a very tricky disorder. Presently, no effective management is available for this condition. Previous studies have reported that acupuncture may be effective for the treatment of OPA. However, its effectiveness is still inconclusive. Thus, this study will aim to assess the effectiveness and safety of acupuncture for OPA. METHODS: A comprehensive literature search for relevant studies will be performed from the databases of PUMBED, EMBASE, CINAHI, Cumulative Index to Nursing and Allied Health Literature, Allied and Complementary Medicine Database, Cochrane Library, Chinese Biomedical Literature Database, China National Knowledge Infrastructure, and other literature sources from inception up to the present. No language limitations will be applied to all literature searches. We will consider all randomized controlled trials (RCTs) and case-controlled trials (CCTs) for assessing the effectiveness and safety of acupuncture for OPA. The primary outcomes include the rates of vision improvement and visual field improvement. The secondary outcomes consist of the increased visual field average sensitivity, pattern visual evoked potential (PVEP) amplitude, and shortened PVEP latency, as well as any expected and unexpected adverse reactions. Risk of bias assessment will be performed by Cochrane risk of bias for RCTs and Newcastle-Ottawa Scale for CCTs. RESULTS: In this study, we will outline details of the aims and methods on the effectiveness and safety of acupuncture for the treatment of OPA. CONCLUSION: The results of this study will summarize the most current evidence of acupuncture for the treatment of patients with OPA. DISSEMINATION AND ETHICS: The results of this study are expected to be published on peer-reviewed journals. This is a literature-based study; therefore, no ethical approval is necessary. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42019135785.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".