Using patient reported outcomes (PROs) to hear the patient: Are we listening?
Bibliographic record
Abstract
e22134 Background: PROs may improve patient centered care by identifying the type and amount of care required. We aimed to examine PROs in a large Canadian province to identify symptoms and needs, and their subsequent management. Methods: A population-based cross-sectional study using administrative data. PROs collected at diagnosis between December 2016 to October 2017 included symptoms, supportive care needs from the Canadian Problem Checklist, demographic and tumor characteristics, and patient management (reported by the clinician). Patients were categorized into six cancer types: breast (Br), lung (L), prostate (Pr), colorectal (CRC), hematological (H), and other (Ot). Multiple linear regression was used to examine predictors of needs. Results: We evaluated 1295 patients. Median age 66 (range19-97) years and 51% men. There were 179 (14%) Br, 394 (30%) L, 83 (6%) Pr, 122 (9%) CRC, 182 (14%) H and 335 (25%) Ot cancers. Pain, tiredness, anxiety and low appetite were the most prevalent moderate to severe symptoms. 66% of patients had at least one need. The most frequent emotional needs were fear and worries. Finances were the highest practical need while strength, sleep, mobility and weight loss were the most common physical need. Information needs were about illness or treatment (Table). Predictors of having higher needs included Br cancer (p.024), higher emotional or physical symptom burden (p < .001), and attending an urban cancer center (p < .001). Health care providers most often provided information (62%), emotional support (43%) and prescriptions (14%) to address PROs, while referrals rates to multidisciplinary providers were low (Table). Conclusions: Many patients reported at least one need and had some moderate to severe symptoms. PROs were addressed mostly with information and emotional support. More understanding of barriers to referral may enhance integration of PROs into routine clinical practice. Need % Referral % Referred Fears and Worries 32 Psychology 2 Finances 13 Social Work 11 Strength 20 Physiotherapy 4 Sleep 20 No service Mobility 20 Physiotherapy 4 Weight loss 22 Dietician 7 Information 27 Information 62 Symptom % score ≤ 4 Pain 40 Pain Clinic 0.8 Tiredness 64 Fatigue Clinic 0.3 Appetite 43 Dietician 7 Anxiety 44 Psychology 2
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".