Delays in Medical Care: A Systematic Review of Determinants, Consequences and Interventions
Bibliographic record
Abstract
Background: Delays in medical care represent a significant public health challenge with substantial impacts on morbidity, mortality, and healthcare costs. These delays can occur at various stages of the care pathway, from symptom recognition by patients to treatment initiation. This systematic review aims to synthesize current evidence on the determinants, consequences, and interventions to reduce these delays through rigorous analysis of published studies. Materials and methods: A comprehensive search was conducted in PubMed, Embase, and Cochrane Library databases for studies published between January 2000 and December 2023. Search strategies combined MeSH terms and keywords: "delays in care," "access to care," "diagnosis," "treatment," and "interventions." Included studies were observational or interventional studies evaluating delayed care in adult patients with quantitative data on delays or consequences. Case studies, literature reviews, and in vitro studies were excluded. Methodological quality was assessed using the Newcastle-Ottawa Scale for observational studies and the Cochrane Risk of Bias Tool for interventional studies. Data extracted included study characteristics, delay determinants, consequences, and interventions. Meta-analyses were performed where appropriate using random-effects models. Results: Multiple factors influence delays in care. For instance, patients in rural areas experienced average delays 2.5 days longer for cardiac symptom consultation compared to urban residents. Patients with lower education levels were 1.8 times more likely to delay consultation for suspected cancer. Regarding consequences, meta-analysis revealed that each day of delay in stroke treatment increased death or disability risk by 5%. Interventions showed promise: patient education programs reduced cardiac symptom consultation delays by 30%, while telemedicine systems decreased specialist referral time for suspected cancer by 20%. Conclusion: Delayed medical care repr
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".