Quality and continuity of information between primary care physicians and rheumatologists
Bibliographic record
Abstract
BACKGROUND: Good communication is central to a high-quality consultation process. We assessed the quality of referral information from primary care physicians (PCPs) to rheumatologists and the quality and timeliness of consultation letters from rheumatologists back to PCPs. METHODS: We sampled referral letters between 2000 and 2013 from 168 PCPs and performed a retrospective chart review of 2430 patients referred to 146 rheumatologists. We assessed the completeness and timeliness of referral and consultation letters. RESULTS: = 745, 31%) comprised the top reasons for referral. Only 55% of referral letters summarized the patients' medical history. Referral letters provided some details of diagnostic tests (51% labs, 34% imaging) but there was underreporting of this information on referral letters. Almost all referral letters (92%) contained details of at least one patient symptom, with the most common complaint being joint pain (54%). Only half of all referral letters provided symptom duration. The PCP only stressed an urgent consultation among 211 patients (9%). Overall, 69% of consultation letters were returned to PCPs within 30 days of consultation visit. CONCLUSION: We found that basic items necessary for appropriate triage, including a description of symptoms or other relevant history and results of investigations were often lacking in referral letters. The delay of receipt of consultation letters may further represent a lost opportunity for coordination and continuity of care, and may affect the quality of care patients receive.
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.
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 | 0.022 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".