Holistic needs assessment in outpatient cancer care: a randomised controlled trial
Bibliographic record
Abstract
DESIGN: Analyst blinded, parallel, multi-centre, randomised controlled trial (RCT). PARTICIPANTS: People with confirmed diagnoses of cancer (head and neck, skin or colorectal) attending follow-up consultation 3 months post-treatment between 2015 and 2020. INTERVENTION: Holistic needs assessment (HNA) or care as usual during consultation. OBJECTIVE: To establish whether incorporating HNA into consultations would increase patient participation, shared decision making and postconsultation self-efficacy. OUTCOME MEASURES: Patient participation in the consultations examined was measured using (a) dialogue ratio (DR) and (b) the proportion of consultation initiated by patient. Shared decision making was measured with CollaboRATE and self-efficacy with Lorig Scale. Consultations were audio recorded and timed. RANDOMISATION: Block randomisation. BLINDING: Audio recording analyst was blinded to study group. RESULTS: 147 patients were randomised: 74 control versus 73 intervention. OUTCOME: No statistically significant differences were found between groups for DR, patient initiative, self-efficacy or shared decision making. Consultations were on average 1 min 46 s longer in the HNA group (respectively, 17 m 25 s vs 15 min 39 s). CONCLUSION: HNA did not change the amount of conversation initiated by the patient or the level of dialogue within the consultation. HNA did not change patient sense of collaboration or feelings of self-efficacy afterwards. HNA group raised more concerns and proportionally more emotional concerns, although their consultations took longer than treatment as usual. IMPLICATIONS FOR PRACTICE: This is the first RCT to test HNA in medically led outpatient settings. Results showed no difference in the way the consultations were structured or received. There is wider evidence to support the roll out of HNA as part of a proactive, multidisciplinary process, but this study did not support medical colleagues facilitating it. TRIAL REGISTRATION NUMBER: NCT02274701.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".