Physician Variability in Treating Pain and Irritability of Unknown Origin in Children with Severe Neurological Impairment
Bibliographic record
Abstract
BACKGROUND: Pain and irritability of unknown origin (PIUO) is a challenging problem for nonverbal children with severe neurological impairments. PIUO is not associated with an identifiable source of nociceptive-inflammatory or neuropathic pain. OBJECTIVE: To assess how physicians use pharmacotherapy to treat PIUO, and to report a pilot study of a standardized approach to investigating and treating PIUO. METHOD: Part 1 of the present study involved independently presenting a case vignette of a patient with PIUO to six experienced physicians who care for children with neurological impairments. They were asked for medication choices and sequences to empirically treat PIUO. Part 2 was a pilot study of a PIUO protocol. Patients followed a standard pathway for PIUO, referred to as the pathway for unknown pain (PUP). The initial drug sequence for the PUP was based on Part 1. RESULTS: In Part 1, physicians responding to the case vignette listed eight medications (atypical antipsychotics, benzodiazepines, gabapentin, methadone, opioids, selective serotonin reuptake inhibitors, tramadol and tricylic antidepressants) and eight empiric drug sequences. In Part 2, eight children with PIUO (six to 17 years of age; five females, three males) were enrolled in a pilot clinic. Only two had been fully evaluated for nociceptive-inflammatory pain sources before enrollment. At the end of the pilot study, four patients were clinically improved and only three required a study medication. DISCUSSION AND CONCLUSION: Even experienced physicians do not agree on a common approach for medical treatment of PIUO. A standardized pathway is feasible and readily implemented. The proposed PUP has the potential to address PIUO and be the basis for future intervention studies.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".