Sarcopenia for outcomes in patients undergoing spinal surgery: A protocol for a systematic review and pooled analysis of observational studies
Bibliographic record
Abstract
BACKGROUND: Sarcopenia is a progressive age-related skeletal muscle disorder characterized by decreased muscle mass and loss of muscle function. Recent studies have shown that sarcopenia is able to predict a variety of clinical outcomes after spinal surgery. Controversy still exists among previous reports in terms of the definition and measurement of sarcopenia, these findings are heterogeneous so far. Therefore, the aim of the current study is to assess the up-to-date evidence of sarcopenia for postoperative outcomes among people undergoing spinal surgery. METHODS AND ANALYSIS: This protocol was carried out based on the preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) statement. It has been pre-registered in PROSPERO with the registration number of CRD42021260459. Three databases (including Pubmed, EMBASE, and Cochrane Library) will be searched from inception through May 10, 2021 to determine related cohort studies examining sarcopenia on multidimensional outcomes in patients undergoing spinal surgery. Major outcomes will be involved including mortality, morbidity, length of stay, postoperative complications or adverse events. DerSimonian & Laird random-effects meta-analysis will be used to calculate pooled odds ratio (OR) for binary data and pooled weighted mean differences (WMDs) or standardized mean differences (SMDs) for continuous data. The Newcastle-Ottawa Scale (NOS) will be used to assess the risk of bias of included studies. Narrative synthesis will be carried out if a pooled analysis is not possible. ETHICS AND DISSEMINATION: Ethical approval is not required for this study as the data involved are from the published literatures. We intend to disseminate or share the results of the study in a peer-reviewed journal or at relevant conferences. PROSPERO REGISTRATION NUMBER: CRD42021260459.
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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 | 0.109 | 0.169 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.023 | 0.031 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".