Let's Get Physical! A Study of General Practitioner's Referral Letters to General Adult Psychiatry — Are Physical Examination and Investigation Results Included?
Bibliographic record
Abstract
BACKGROUND/AIM: The authors previously conducted a survey of psychiatrists' attitudes to physical examination and investigations of out-patients. The most common reason for not performing such investigations was the expectation that they had already been undertaken by the general practitioner (GP). We decided to test this theory. METHOD: A series of GP out-patient referral letters to general psychiatry was examined to establish whether findings from physical examination and investigations had been included. RESULTS: One hundred and three letters were examined. None of the letters contained information relating to a physical examination. Only one in twenty had information on investigations despite 4 out of 10 patients in the sample presenting to the GP with somatic symptoms. CONCLUSION: Details of physical examination and blood tests are not routinely included in referral letters to general psychiatry. This may lead to missed diagnoses of primary or secondary physical illness in psychiatric presentations. Unless it is clearly stated in the GP referral letter, it is unwise to assume that necessary investigations to exclude physical causes of presenting symptoms have been performed. Suggestions are made to improve communication between GPs and psychiatrists.
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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 | 0.011 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".