Correction to: Clinician responses to cannabis use during pregnancy and lactation: a systematic review and integrative mixed-methods research synthesis
Bibliographic record
Abstract
Family Practice, cmab146, doi:10.1093/fampra/cmab146 In the originally published version of this manuscript, the Conflict of interest statement was incorrect and read: There are no conflicts to declare. From April to December 2019, JP was employed as a Research Analyst at PureSinse Inc (a licensed cannabis producer). She does not currently hold any financial or personal connection to PureSinse,which is no longer in operation. This has now been updated as follows: From April to December 2019, J. Panday was employed as a Research Analyst at PureSinse Inc (a licensed cannabis producer). She does not hold any remaining financial or personal connection to PureSinse, which is no longer in operation. From May 2021 - February 2022, J. Panday was employed as a freelance research coordinator by Avail Cannabis Clinics, a privately owned network of medical clinics, to prepare and submit ethics applications for research related to the use of cannabis to alleviate PTSD symptoms in military populations. Ms. Panday was compensated hourly for this work, which has concluded. She does not hold any remaining financial or personal connection to Avail.
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 | 0.086 | 0.426 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.016 | 0.022 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.079 | 0.015 |
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".