Association between insomnia patients’ pre-treatment characteristics and their responses to distinctive treatment sequences
Bibliographic record
Abstract
STUDY OBJECTIVES: It is common to provide insomnia patients a second treatment when the initial treatment fails, but little is known about optimal treatment sequences for different patient types. This study examined whether pre-treatment characteristics/traits predict optimal treatment sequences for insomnia patients. METHODS: A community sample of 211 adults (132 women; Mage = 45.6 ± 14.9 years) with insomnia were recruited. Patients were first treated with behavioral therapy (BT) or zolpidem (Zol). Non-remitting BT recipients were randomized to a second treatment with either Zol or cognitive therapy; non-remitting Zol recipients underwent BT or Trazodone as a second treatment. Remission rates were assessed at the end of the first and second 6-week treatments. We then compared the remission rates of dichotomous groups formed on the basis of gender, age, pretreatment scores on SF36 and Multidimensional Fatigue Scale, the presence/absence of psychiatric/medical comorbidities or pain disorders, and mean subjective sleep duration and efficiency within and across treatment sequences. RESULTS: Lower remission rates were noted for those: with a pain disorder, poor mental health perceptions, high MFI fatigue scores, and lower sleep times and efficiencies. Patients with a pain disorder responded best to the BT-to-Zol sequence, whereas patients with more mental impairment, severe fatigue, short sleep, and low sleep efficiency responded poorly to treatment starting with BT. CONCLUSIONS: Pain, fatigue, poor mental health status, and subjective sleep duration and efficiency all affect response to different insomnia treatment sequences. Findings may guide clinicians in matching insomnia treatments to their patients. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01651442, Protocol version 4, April 20, 2011, registered June 26, 2012, https://clinicaltrials.gov/ct2/show/NCT01651442?rslt=With&type=Intr&cond=Insomnia&cntry=US&state=US%3ACO&city=Denver&age=12&draw=2&rank=1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".