Identifying potentially eligible subjects for research: paper-based logs versus the hospital administrative database
Bibliographic record
Abstract
INTRODUCTION: The Canadian Perinatal Network (CPN) is a national database focused on threatened very pre-term birth. Women with one or more conditions most commonly associated with very pre-term birth are included if admitted to a participating tertiary perinatal unit at 22 weeks and 0 days to 28 weeks and 6 days. METHODS: At BC Women's Hospital and Health Centre, we compared traditional paper-based ward logs and a search of the Canadian Institute for Health Information (CIHI) electronic database of inpatient discharges to identify patients. RESULTS: The study identified 244 women potentially eligible for inclusion in the CPN admitted between April and December 2007. Of the 155 eligible women entered into the CPN database, each method identified a similar number of unique records (142 and 147) not ascertained by the other: 10 (6.4%) by CIHI search and 5 (3.2%) by ward log review. However, CIHI search achieved these results after reviewing fewer records (206 vs. 223) in less time (0.67 vs. 13.6 hours for ward logs). CONCLUSION: Either method is appropriate for identification of potential research subjects using gestational age criteria. Although electronic methods are less time-consuming, they cannot be performed until after the patient is discharged and records and charts are reviewed. Each method's advantages and disadvantages will dictate use for a specific project.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".