What is the clinical significance of low serum amylase? Systematic review of the conditions associated with low serum amylase
Bibliographic record
Abstract
Objective: Most studies have assessed the impact of elevated serum amylase levels in clinical practice, but only a few have investigated the significance of low serum amylase. We therefore, aimed to review the literature to understand the conditions associated with low serum amylase and its clinical relevance. Method: This systematic review was performed in accordance with the criteria established in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search was conducted on Medline and Embase databases until November 2022. After identifying relevant titles, abstracts were read and data of eligible articles retrieved. The conditions associated with low serum amylase were evaluated. The quality of the studies was assessed using the Newcastle-Ottawa Score. Results: Our search strategy identified 19 studies including a total of 15 097 patients for systematic review. All the studies were observational including two studies which used secretin-induced test. The main conditions associated with low serum amylase were diabetes mellitus (n=9), metabolic syndrome (n=3), chronic pancreatitis (CP) (n=3), non-alcoholic fatty liver disease (n=2) and obesity (n=1). Low serum amylase showed a high specificity (94%) with low sensitivity (38.7%-59%) in diagnosing chronic pancreatitis. Conclusion: This systematic review revealed a unique insight into the relevance of low serum amylase in clinical practice. Low serum amylase can be a useful adjunct test in the assessment of patients with CP, pancreatic exocrine insufficiency, diabetes mellitus and metabolic syndrome.
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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 | 0.014 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".