The benefits and risks of structuring and coding of patienthistories in the electronic clinical record: protocol for asystematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Data in medical records have in part been recorded in structured and coded forms for some decades. However, the patient history is as yet largely recorded in an uncoded format. There is a need to consider the optimal balance of use of free text and coded data in the patient history. This review protocol summarises our plans to identify, critically appraise and synthesise evidence relating to approaches taken to introduce structure and coding within patient histories in electronic health records, and the empirically demonstrated benefits and risks of structuring and coding of patient histories in health records. OBJECTIVES: To determine how structured and coded data are being introduced for the recording of patient histories, the benefits observed where structuring and coding have been introduced and the risks encountered when structuring and coding are introduced. METHODS: We will search the following databases for evidence of published and unpublished material: CINAHL; EMBASE; Google Scholar; IndMED; LILACS; MEDLINE; NIHR; Paklit and PsycINFO. We will, depending on the study designs employed, use the Cochrane EPOC, Joanna Briggs Institute (JBI) and Newcastle-Ottawa instruments to critically appraise studies. Data synthesis is likely to be undertaken using a narrative approach, although meta-analysis will also be undertaken if appropriate and if the data allow this. RESULTS: This protocol should represent a reproducible approach to reviewing the literature regarding structuring and coding in patient histories. We anticipate that we will be able to report results in early 2011. CONCLUSION: The review should offer increased clarity and direction on the optimal balance between structuring/coding and free text recording of data relating to the patient history.
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.
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 | 0.051 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 it