Protocol to describe the analysis of text-based communication in medical records for patients discharged from intensive care to hospital ward
Bibliographic record
Abstract
INTRODUCTION: Effective communication during hospital transitions of patient care is fundamental to ensuring patient safety and continuity of quality care. This study will describe text-based communication included in patient medical records before, during and after patient transfer from the intensive care unit (ICU) to a hospital ward (n=10 days) by documenting (1) the structure and focus of physician progress notes within and between medical specialties, (2) the organisation of subjective and objective information, including the location and accessibility of patient data and whether/how this changes during the hospital stay and (3) missing, illegible and erroneous information. METHODS: This study is part of a larger mixed methods prospective observational study of ICU to hospital ward transfer practices in 10 ICUs across Canada. Medical records will be collected and photocopied for each consenting patient for a period of up to 10 consecutive days, including the final 2 days in the ICU, the day of transfer and the first 7 days on the ward (n=10 days). Textual analysis of medical record data will be completed by 2 independent reviewers to describe communication between stakeholders involved in ICU transfer. ETHICS AND DISSEMINATION: Research ethics board approval has been obtained at all study sites, including the coordinating study centre (which covers 4 Calgary-based sites; UofC REB 13-0021) and 6 additional study sites (UofA Pro00050646; UBC PHC Hi4-01667; Sunnybrook 336-2014; QCH 20140345-01H; Sherbrooke 14-172; Laval 2015-2171). Findings from this study will inform the development of an evidence-based tool that will be used to systematically analyse the series of notes in a patient's medical record.
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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 | 0.060 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.129 | 0.043 |
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".