Assessment of accuracy of data obtained from patient-reported questionnaire (PRQ) compared to electronic patient records (EPR) in patients with lung cancer.
Bibliographic record
Abstract
40 Background: Cigarette smoking, alcohol consumption and co-morbidities are important determinants of health in lung cancer patients. The gold standard for obtaining accurate data is PRQ. The purpose of this study is to ascertain the accuracy of abstracting health-related behaviour data from retrospective chart review compared to data directly obtained from PRQ in a lung cancer patient population. Methods: 731 lung cancer patients completed a PRQ related to lifetime tobacco use, alcohol consumption and co-morbidity. Relevant smoking, alcohol and co-morbidity data was collected independently from EPR. Results: Ever/never status for smoking showed almost perfect agreement (k=0.95) between PRQ and EPR and surpassed all other health behavioural measures and co-morbidity agreement values. Both the sensitivity and specificity were high (0.94 and 0.99 respectively). The calculation of pack-years from EPR and PRQ showed substantial agreement (k=0.77); However, categorizing the smoking status into current/ former / never, resulted in moderate agreement (k=0.46). Alcohol ever/ never status agreement was moderate (0.43) with high sensitivity (0.90) but low specificity (0.50). Agreement for co-morbidities varied by condition showing moderate to substantial agreement for hypertension (K=0.57), heart attack (K=0.80) and diabetes (K=0.76) while fair to slight agreement (K<0.4) was seen in the others. Specificity was 0.86 or higher for co-morbidity conditions and was consistently higher than the sensitivity. Conclusions: EPR may be used as a reliable surrogate to PRQ in determining ever/never smoking status and lifetime smoking exposure. Evaluation of current/former/never smoking status and alcohol consumption is best determined by PRQ. Diabetes, hypertension and heart attack are more accurately reported in the PRQ than other co-morbidities. Patients tend to report absence of a medical condition more accurately than the presence of it. Missing EPR data related to smoking pack years, alcohol consumption and lung co-morbidities is concerning and suggests more synoptic reporting by physicians would improve opportunities for research.
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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 | 0.033 | 0.128 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".