The pathway and characteristics of patients with non-specific symptoms of cancer: a systematic review
Bibliographic record
Abstract
BACKGROUND: Non-specific symptoms are common and often sign of a non-serious disease. Because of this, patients with non-specific symptoms of cancer (NSSC) present a challenge for general practitioners (GP). Studies describing characteristics of patients with NSSC have been done after fast-track pathways were created to diagnose and treat patients with NSSC. This study reviews characteristics of patients with NSSC and their patient pathways. MATERIALS AND METHODS: Database searches of Embase, Cochrane, PubMed, Cinahl and Web of Science were performed. Search terms used were cancer, patient pathway, and NSSC with their synonyms. The flow diagram Preferring Reporting Items for Systematic Review was applied to the systematic search. The Newcastle-Ottawa Assessment Scale (NOS) was used to compare the quality of the included studies. RESULTS: Twelve studies met the inclusion criterias. All studies were considered to be of high methodological quality. Patient Pathway: 11-35% of patients were diagnosed with cancer. Median number of days through diagnostic process was 7-10. PATIENT CHARACTERISTICS: The most prevalent cancers included hematological-(14-30%), gastrointestinal-(13-23%) and lung cancers (13%). Rheumatological, musculoskeletal and gastrointestinal diseases were among the most common non-malignant diseases diagnosed. Weight loss, fatigue, pain and loss of appetite were the most common symptoms. Cardiovascular diseases, lung diseases, diabetes and previous diagnosed cancer were the most common comorbidities. Mean age of included patients was 60-72 years. CONCLUSION: Limited number of studies were found and they lacked sufficient heterogenic data to conduct a metaanalysis. Symptoms, diagnoses, age and gender were described with some heterogenic results. Further studies should be conducted to gather broader knowledge about patients with NSSC.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".