Why do family physicians fail to detect renal impairment?
Bibliographic record
Abstract
OBJECTIVE: To investigate why many patients with renal impairment (30.7%) were not recognized by their family physicians despite an earlier educational intervention on detecting renal impairment; and to determine whether certain factors related to physicians, patients, or the intervention itself were associated with whether renal impairment was detected. DESIGN: Qualitative approach using grounded theory. SETTING: A Health Service Organization in Ottawa, Ont. PARTICIPANTS: A purposeful sample of six family physicians. METHODS: In semistructured interviews, participants were asked to describe the workup ordered and their decision-making processes for patients in whom they had recently detected renal impairment. They were also asked to evaluate the six components of an educational intervention designed to help them to detect renal impairment. Finally, one patient's chart was reviewed (a chart containing a laboratory report noting an abnormal result for kidney function and having no indication that renal impairment had been recognized) to identify reasons for lack of detection. RESULTS: Most physicians did not investigate every patient with renal impairment (glomerular filtration rate of < 78 mL/min) in the same way because they took individual patient factors into consideration. Reasons for not detecting renal impairment were "managed differently" or "missed," with the former being the most common. The educational intervention physicians remembered most often was chart rounds, and these were viewed as helpful. "Missed" cases were more often deliberately managed differently than unintentionally not detected. CONCLUSION: Physicians used various approaches to detect and manage renal impairment despite interventions that recommended a consistent procedure.
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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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".