OP23 * AN AUDIT TO ASSESS PSYCHIATRIC CONDITIONS THAT MANIFEST IN PATIENTS WITH HIGH-GRADE BRAIN TUMOURS DURING TREATMENT
Bibliographic record
Abstract
INTRODUCTION: We recorded over a 9 month period the number of referrals made for specialist advice on how to treat a patient's psychiatric condition from those attending our neuro-oncology clinics. The patient's characteristics were reviewed to understand the causes, produce local referral guidelines, and identify at risk patients at the earliest opportunity. METHOD: We prospectively gathered names of patients referred as outpatients to our Psychological Medicine Department between 1st September 2011 and 31st May 2012. The notes were examined for patient characteristics, tumour biology, current treatment, psychiatric presentation, past psychiatric history, concurrent seizure medication, and psychiatric treatment recommended. RESULTS: During the monitored time we identified 74 patients, 11 of which had a past or current psychiatric history, 8 of which were referred to psychological medicine for specialist advice. Of the 3 not referred, 1 had mild dementia, and the remaining 2 had issues with alcoholism and were managed medically. Only 1 patient was referred having no previous psychiatric history. Most patients had Grade 4 tumours and were having chemo-radiotherapy, but some had chemotherapy only or surgery. Current psychiatric conditions expressed were similar to the patient's past psychiatric condition. Steroid-induced psychosis was not uncommon, and 3 patients benefited from the use of anti-psychotic therapy, or change of steroid. CONCLUSION: The response to diagnosis and treatment appeared to trigger most referrals, however, all the patients completed their treatment safely, and without any interruptions. We are introducing a screening tool used pre-treatment to identify at risk patients, and refer for advice early.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".