Evaluation of an occupational medicine patient consultation note assessment tool
Bibliographic record
Abstract
BACKGROUND: Medical education focuses on assessment, diagnosis and management of various clinical entities. The communication of this information, particularly in the written form, is rarely emphasized. Though there have been assessment tools developed to support medical learner improvement in this regard, none are oriented to occupational medicine (OM) practice. AIMS: This study was aimed to develop and evaluate an assessment tool for consultation letters, by modifying a previously validated assessment tool to suit practice in OM. METHODS: Using an iterative process, OM specialists added to the Consultation Letter Rating Scale (CLRS) of the Royal College of Physicians and Surgeons of Canada (henceforth abbreviated as RC) additional questions relevant to communication in the OM context. The tool was then used by two OM specialists to rate 40 anonymized OM clinical consultation letters. Inter-rater agreement was measured by percent agreement, kappa statistic and intraclass correlation. RESULTS: There was generally good percent agreement (>80% for the majority of the RC and OM questions). Intraclass correlation for the five OM questions total scores was slightly higher than the intraclass correlations for the five RC questions (0.59 versus 0.46, respectively), suggesting that our modifications performed at least as well as the original tool. CONCLUSIONS: This new tool designed specifically for evaluation of patient consultation notes in OM provides a good option for medical educators in a variety of practice areas in providing non-summative, low-stakes assessment and/or feedback to nurture increased competency in written communication skills for postgraduate trainees in OM.
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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 | 0.026 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".