Non‐recruitment to and selection bias in studies using echocardiography in haemodialysis patients
Bibliographic record
Abstract
BACKGROUND: It is unknown whether patients recruited to renal cardiac imaging studies are fully representative of the investigated population and whether there are differences in characteristics and survival between participants and non-participants (excluded or declined consent). Subjects and Methods Four hundred thirty-five maintenance haemodialysis patients were screened in an observational, prospective study. Baseline demographics, laboratory results, social deprivation scores and survival data were collected from patient records. All patients were followed-up until death, renal transplantation or 16 November 2015. RESULTS: Forty-four patients were excluded (16 language barrier, 10 mental incapacity, 9 severe co-morbid illness and 9 because of immobility), 172 patients declined consent (84% due to reluctance to attend for an extra visit) and 219 patients were recruited. Excluded patients had a lower mean haemoglobin (10.2 g/dL vs 10.7 g/dL), phosphate (4.15 mg/dL vs 4.74 mg/dL), albumin (3.6 g/dL vs 3.8 g/dL) and higher C-reactive protein (3.2 mg/dL vs 1.6 mg/dL) compared with recruited patients. No difference was identified between groups for Charleston comorbidity index (P = 0.115) or social deprivation scores. After a median follow-up of 29.7 (25th-75th percentile, 21.1-34.3) months, there were 141 deaths. In a multivariable Cox regression model adjusting for BMI, age, Charleston comorbidity index, haemoglobin, albumin, smoking status and diabetes mellitus, patients who declined consent had an adjusted HR of 1.70, 95% CI 1.10-2.52, and excluded patients had an adjusted HR of 1.30, 95% CI 0.75-2.25, for all-cause mortality compared with recruited patients. CONCLUSIONS: Patients recruited to the study had longer survival compared with non-participants. Research studies should document phenotypes of non-participants to aid interpretation and generalizability of results.
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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 | 0.108 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".