Triage tools to inform the prioritisation of physical health services following a diagnosis of cancer: a scoping review
Bibliographic record
Abstract
PURPOSE: Many people face multiple cancer- and treatment-related sequalae. Triage and referral to physical health services can manage such consequences, but a comprehensive understanding of available triage tools is lacking. This review (i) identifies tools used to triage to physical health services, (ii) maps tool characteristics and application outcomes and (iii) summarises existing gaps. METHODS: A systematic search was conducted (three databases, April 2024). Articles were included if they used a tool to triage to physical health services. Tools were classified by triaged disciplines (i.e., diet, exercise, physical rehabilitation, multidisciplinary) and screened physical impairments (e.g., malnutrition). Tool characteristics (e.g., triage method) and application outcomes (i.e., reach, triage rates) were extracted. RESULTS: Of 23,369 records retrieved, 67 studies were included. Studies comprised 78 instances of tool use (64 unique tools), where n = 33 triaged to dietetics (42%), n = 6 exercise (8%), n = 11 physical rehabilitation (14%), and n = 28 a combination of health disciplines (36%). Mean age was 65 years. Most tools were used during-treatment (45%), in hospital settings (62%), measured malnutrition/physical function (60%) and used single cut-off scores (68%). Reach and triage rates varied, with exercise (reach = 89%) and diet (triage = 63%) rates highest. CONCLUSION: Many physical health triage tools exist, most solely for dietetics, with heterogeneous characteristics and application outcomes. Updated tools are needed for triage to exercise/physical rehabilitation, multiple age cohorts across the cancer continuum, and that potentially use multiple cut-off scores. Cancer care professionals can use this compendium to identify which tool characteristics best suit their healthcare setting, for optimal outcomes.
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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 | 0.019 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".