<!-- hidden notes: But what you need is links in images For images in tables: [[Label text \|]] ---> ```mermaid graph TD A["Protocol"] B["Search strategy"] C["Search"] D["Deduplication"] E["Screening"] A --> B B --> C C --> D D --> E class A,B,C,D,E internal-link; ``` <div align = "center"><h1 style="font-size:20;">Wʜᴀᴛ ɪs ᴛʜᴇ ᴏᴘᴛɪᴍᴀʟ ɢʀᴀꜰᴛ ᴄʜᴏɪᴄᴇ ꜰᴏʀ ᴀɴᴛᴇʀɪᴏʀ ᴄʀᴜᴄɪᴀᴛᴇ ʟɪɢᴀᴍᴇɴᴛ ʀᴇᴏɴꜱᴛʀᴜᴄᴛɪᴏɴ ꜱᴜʀɢᴇʀʏ?</h1> </div> ###### LICENSE >> [!Note]     $\quad$ **CC BY-NC-ND 4.0** >> **Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International** >> >> This license requires that reusers give credit to the creator, [Dong Woon Kim](https://orcid.org/0000-0002-5191-8685). It allows reusers to copy and distribute the material in any medium or format in unadapted form and for noncommercial purposes only. >> >> **BY**: Credit must be give to [Dong Woon Kim](https://orcid.org/0000-0002-5191-8685), the creator. >> **NC**: Only noncommercial use of your work is permitted. *Noncommercial means not primarily intended for or directed towards commercial advantage or monetary compensation.*) >> **ND**: No derivatives or adaptations of your work are permitted.) >> >><a link="color:red" href="https://creativecommons.org/licenses/by-nc-nd/4.0/">See the License Deed</a> Protocol -------------- - PROSPERO - Cochrane Reviews ```bash py -m pip install virtualenv py -m venv .venv ./.venv/Scripts/Activate.ps1 pip install -r requirements.txt ``` Systematic Literature Review =============================== Search strategy #done -------------------------- The search strategy terms were as designed separately for each acronym in PICOS format[^1]. The search terms were grouped into strings separated by the Boolean operator, OR depending on their inclusion within each the PICO framework: *population/participants*, *intervention*, *outcomes* and *study design* were concatenated using the appropriate Boolean operators as delimiters before adding the *comparator* search terms and queries for the individual graft types. The search strategy was developed first for PubMed/MEDLINE, and then using regular expressions to find and replace the appropriate differences in query syntax, translated into search strategies for Embase and Web of Science Core Collection databases. Attention to detail was placed to ensure that the search terms remained exactly the same, and more importantly, that their meaning, carried by the logical *expressions* with the Boolean operators and field type values. A python script was written to facilitate a possible update search prior to publication would be executed promptly and in exactly the same way. <div align = "center"> <img width="100%" height="50%" alt="search_strategy_documentation_1" src="https://github.com/user-attachments/assets/cf2b6cde-3946-4532-925c-4f7d2eaa7a77" /> </div> | input | script | output | | | :----------: | :-------: | :----------------------: | --- | | | pubmed.py | pubmed.xml, pm/query.txt | | | pm/query.txt | embase.py | embase.xml, em/query.txt | | | pm/query.txt | wos.py | wos.xml, wos/query.txt | | | | | | | Search #done ----------- The literature search was performed on February 9th-12th for each of the six graft types across three databases. There were 503 studies for PubMed, 578 for Embase, and 228 from Web of Science Core Collection for the six graft types combined. The search was repeated again in March using API keys to cross-reference the initial results. The most recent update to the search---at the time---f this writing was on March 19th. The process by which the identification of studies through the search to the full-text screening stage is depicted in a flowchart, according to PRISMA guidelines, and adapted for the purposes particular to this project ([Figure 1](#^figure1))) ## Script 1  Deduplication #done ----------------------- A custom python script had been written during a previous project, due to the lack of an intuitive method and the wide range of discrepancies found in results across the major commercial reference management software, in which built-in deduplication functions. The results were vetted against manual deduplication of the same records and were found to deliver the most accurate results. The script also allows for specific columns to be picked as the basis by which duplicate records are identified and removed. Furthermore, a flowchart of the total number of records that were duplicates, non-duplicates, and blank is generated automatically at every stage and a CSV file of these separate spreadsheet files of records are produced. For each column chosen, the script was designed to separate the `null` or blank values from those that are `non-null` values. From the latter, the non-duplicate records and filtered out from those that a duplicate record is found. The duplicate records were kept or removed. The null, non-duplicate (unique) and kept duplicate <!--revise the script to **merge** duplicate records!--> records underwent further deduplication. The last name of the first author, year of publication and punctuation removed and shortened title were concatenated into a `key` column for the 2nd tier of deduplication For this project, the columns or field names were designated to one of three sequential tiers: **DOI**, then a composite-key comprised of the last name of the first **author**, the 35 character-shortened **title** of the article without punctuation and **year**. The third pass was done based on unique **identifier numbers**, firstly PMID, then clinical trial identification numbers. Out of 1,309 records identified, 1,115 duplicate records were found, 624 records were removed and 585 unique records were screened ([Table 1](#^table1)). **Table 1** ^table1 | column | records | null | duplicates | removed | unique | remaining | | :-------------------- | :-----: | :--: | :--------: | :-----: | :----: | :-------: | | DOI | 1,309 | 238 | 902 | 599 | 472 | 710 | | Title + Author + Year | 710 | 123 | 133 | 78 | 509 | 632 | | Clinical Trial ID | 632 | 0 | 80 | 44 | 585 | 585 | **Fig 1** PRISMA Flowchart of the identification of studies included ^figure1  Screening #todo ---------------- Data Collection ================ Database design and management #done Data entry form #todo Data extraction, transformation, and preparation Meta-analysis =============== References --------- **Living systematic reviews**: - *PRISMA-LSR* PRISMA 2020 extension for living systematic reviews (LSR). **Network meta-analysis**: - *PRISMA-NMA* - *Cochrane Handbook for Systematic Reviews of Interventions*. Higgins (2019) [1]: https://systematicreviewtools.com/