Effects of oncological care pathways in primary and secondary care on patient, professional and health systems outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Pathways are frequently used to improve care for cancer patients. However, there is little evidence about the effects of pathways used in oncological care. Therefore, we performed a systematic review and meta-analysis aiming to identify, and synthesise existing literature on the effects of pathways in oncological care. Methods All patients diagnosed with cancer in primary and secondary care whose treatment can be characterized as the strategy “care pathways” are included in this review. A systematic search in seven databases was conducted to gather evidence. Studies were screened by two independent reviewers. Study outcomes regarding “patient outcomes” and “costs” were extracted from each study. Results Out of 12,689 search results, we selected 158 articles eligible for full text assessment. The remaining 10 studies represented 4,786 patients. Most studies were conducted in secondary care. LOS was the most common used indicator for patients outcomes, and was reported in five studies. Meta-analysis based on subgroups showed an overall shorter LOS regarding gastric cancer (WMD: -2.75, CI: -4.67–-0.83) and gynaecological cancer (WMD: -1.58, CI: -2.10–-1.05). Costs were reported in six studies and most studies reported lower costs for pathway groups. Conclusions Despite the differences between the included studies, we were able to present an evidence base for cancer care pathways performed in secondary care regarding the positive effects of LOS in favour of cancer care pathways.
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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 | 0.023 | 0.055 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.058 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".