<scp>M</scp>aternal <scp>O</scp>pioid <scp>T</scp>reatment: <scp>H</scp>uman <scp>E</scp>xperimental <scp>R</scp>esearch (<scp>MOTHER</scp>)—approach, issues and lessons learned
Bibliographic record
Abstract
AIMS: The Maternal Opioid Treatment: Human Experimental Research (MOTHER) project, an eight-site randomized, double-blind, double-dummy, flexible-dosing, parallel-group clinical trial is described. This study is the most current--and single most comprehensive--research effort to investigate the safety and efficacy of maternal and prenatal exposure to methadone and buprenorphine. METHODS: The MOTHER study design is outlined, and its basic features are presented. CONCLUSIONS: At least seven important lessons have been learned from the MOTHER study: (i) an interdisciplinary focus improves the design and methods of a randomized clinical trial; (ii) multiple sites in a clinical trial present continuing challenges to the investigative team due to variations in recruitment, patient populations and hospital practices that, in turn, differentially impact recruitment rates, treatment compliance and attrition; (iii) study design and protocols must be flexible in order to meet the unforeseen demands of both research and clinical management; (iv) staff turnover needs to be addressed with a proactive focus on both hiring and training; (v) the implementation of a protocol for the treatment of a particular disorder may identify important ancillary clinical issues worthy of investigation; (vi) timely tracking of data in a multi-site trial is both demanding and unforgiving; and (vii) complex multi-site trials pose unanticipated challenges that complicate the choice of statistical methods, thereby placing added demands on investigators to effectively communicate their results.
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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.088 | 0.020 |
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".