Submitting Manuscripts to Biomedical Journals: Common Errors and Helpful Solutions
Bibliographic record
Abstract
This article reviews common, but avoidable, errors that authors may make when submitting to a health care–focused, biomedical journal (eg, chiropractic, medicine, nursing, and physical therapy). As editors, we offer suggestions for improving the quality of manuscripts submitted to biomedical journals, provide suggestions for how to avoid making errors, and recommend effective writing and submission strategies. Common errors in the following sections are discussed: title, abstract, key words, introduction, methods, results, discussion, conclusion, acknowledgments, references, tables, figures, cover letter, format and writing, submission processes, communication with the editor, revision processes, and proof processes. This article includes a checklist that authors may use before submission and that peer reviewers may use for general critique of a manuscript. The goal of this article is to assist authors with successful manuscript submission and eventual publication.
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 | 0.150 | 0.640 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 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".