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Alginate dressings for donor sites of split‐thickness skin grafts

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Appendices

Appendix 1. British National Formulary Wound Dressings Classification

The British National Formulary categorises dressings under the headings below (BNF 2017). Within each category there is a listing of dressings currently available.

Basic wound contact dressings

  • Low adherence dressings

  • Absorbent dressings

Advanced wound dressings

  • Hydrogel dressings

  • Sodium hyaluronate dressings

  • Vapour‐permeable films and membranes

  • Soft polymer dressings

  • Hydrocolloid dressings

  • Foam dressings

  • Alginate dressings

  • Capillary‐action dressings

  • Odour‐absorbent dressings

Antimicrobial dressings

  • Honey

  • Iodine

  • Silver

  • Other antimicrobials

Specialised dressings

  • Protease‐modulating matrix dressings

  • Silicone keloid dressings

Appendix 2. The Cochrane Central Register of Controlled Trials (CENTRAL) draft search strategy

#1 MeSH descriptor: [Bandages] explode all trees
#2 MeSH descriptor: [Alginates] explode all trees
#3 (alginate* or activheal or algisite or algosteril or biatain or cutimed or curasorb or kaltostat or kendall or melgisorb or seasorb or sorbalgon or sorbsan or suprasorb or tegaderm or tegagel or urgosorb):ti,ab,kw
#4 {or #1‐#3}
#5 MeSH descriptor: [Skin Transplantation] explode all trees
#6 MeSH descriptor: [Transplantation, Autologous] explode all trees
#7 MeSH descriptor: [Transplant Donor Site] explode all trees
#8 (((split next thick*) or split‐thick* or "split skin" or split‐skin or "partial dermal" or partial‐dermal or (partial next thick*) or partial‐thick*) near/3 graft*):ti,ab,kw
#9 ((skin or derm*) next transplant*):ti,ab,kw
#10 STSG:ti,ab,kw
#11 "donor site":ti,ab,kw
#12 {or #5‐#11}
#13 {and #4, #12} in Trials

Appendix 3. The Cochrane tool for assessing risk of bias

1. Was the allocation sequence randomly generated?

Low risk of bias

The investigators describe a random component in the sequence generation process such as: referring to a random‐number table; using a computer random‐number generator; coin tossing; shuffling cards or envelopes; throwing dice; drawing of lots.

High risk of bias

The investigators describe a non‐random component in the sequence generation process. Usually, the description would involve some systematic, non‐random approach, for example: sequence generated by odd or even date of birth; sequence generated by some rule based on date (or day) of admission; sequence generated by some rule based on hospital or clinic record number.

Unclear

Insufficient information about the sequence generation process provided to permit a judgement of low or high risk of bias.

2. Was the treatment allocation adequately concealed?

Low risk of bias

Participants and investigators enrolling participants could not foresee assignment because one of the following, or an equivalent method, was used to conceal allocation: central allocation (including telephone, web‐based and pharmacy‐controlled randomisation); sequentially numbered drug containers of identical appearance; sequentially numbered, opaque, sealed envelopes.

High risk of bias

Participants or investigators enrolling participants could possibly foresee assignments and thus introduce selection bias, such as allocation based on: use of an open random allocation schedule (e.g. a list of random numbers); assignment envelopes without appropriate safeguards (e.g. envelopes were unsealed, non‐opaque or not sequentially numbered); alternation or rotation; date of birth; case record number; any other explicitly unconcealed procedure.

Unclear

Insufficient information provided to permit a judgement of low or high risk of bias. This is usually the case if the method of concealment is not described or not described in sufficient detail to allow a definite judgement, for example, if the use of assignment envelopes is described, but it remains unclear whether envelopes were sequentially numbered, opaque and sealed.

3. Blinding of participants/care providers/outcome assessors ‐ was knowledge of the allocated interventions adequately prevented during the study?

Low risk of bias

Any one of the following.

  • No blinding, but the review authors judge that the outcome and the outcome measurement are not likely to be influenced by lack of blinding.

  • Blinding of participants and key study personnel ensured, and unlikely that the blinding could have been broken.

  • Either participants or some key study personnel were not blinded, but outcome assessment was blinded and the non‐blinding of others is unlikely to introduce bias.

High risk of bias

Any one of the following.

  • No blinding or incomplete blinding, and the outcome or outcome measurement is likely to be influenced by lack of blinding.

  • Blinding of key study participants and personnel attempted, but likely that the blinding could have been broken.

  • Either participants or some key study personnel were not blinded, and the non‐blinding of others is likely to introduce bias.

Unclear risk of bias

Either of the following.

  • Insufficient information provided to permit judgement of low or high risk of bias.

  • The study did not address this outcome.

4. Were incomplete outcome data adequately addressed?

Low risk of bias

Any one of the following.

  • No missing outcome data.

  • Reasons for missing outcome data are unlikely to be related to true outcome (for survival data, censoring unlikely to be introducing bias).

  • Missing outcome data are balanced in numbers across intervention groups, with similar reasons for missing data across groups.

  • For dichotomous outcome data, the proportion of missing outcomes compared with the observed event risk is not enough to have a clinically relevant impact on the intervention effect estimate.

  • For continuous outcome data, a plausible effect size (difference in means or standardised difference in means) among missing outcomes is not enough to have a clinically relevant impact on the observed effect size.

  • Missing data have been imputed using appropriate methods.

High risk of bias

Any one of the following.

  • Reason for missing outcome data are likely to be related to the true outcome, with either an imbalance in numbers or reasons for missing data across intervention groups.

  • For dichotomous outcome data, the proportion of missing outcomes compared with the observed event risk is enough to induce clinically relevant bias in the intervention effect estimate.

  • For continuous outcome data, a plausible effect size (difference in means or standardised difference in means) among missing outcomes is enough to induce clinically relevant bias in the observed effect size.

  • 'As‐treated' analysis is done with a substantial departure of the intervention received from that assigned at randomisation.

  • Potentially inappropriate application of simple imputation.

Unclear

Either of the following:

  • Insufficient reporting of attrition/exclusions to permit a judgement of low or high risk of bias (e.g. number randomised not stated, no reasons for missing data provided).

  • The study did not address this outcome.

5. Are reports of the study free of suggestion of selective outcome reporting?

Low risk of bias

Either of the following.

  • The study protocol is available and all of the study’s prespecified (primary and secondary) outcomes that are of interest in the review have been reported in the prespecified way.

  • The study protocol is not available but it is clear that the published reports include all expected outcomes, including those that were prespecified (convincing text of this nature may be uncommon).

High risk of bias

Any one of the following.

  • Not all of the study’s prespecified primary outcomes have been reported.

  • One or more primary outcomes are reported using measurements, analysis methods or subsets of the data (e.g. subscales) that were not prespecified.

  • One or more reported primary outcomes was/were not prespecified (unless clear justification for their reporting is provided, such as an unexpected adverse effect).

  • One or more outcomes of interest in the review is/are reported incompletely so that they cannot be entered in a meta‐analysis.

  • The study report fails to include results for a key outcome that would be expected to have been reported for such a study.

Unclear

Insufficient information is provided to permit a judgement of low or high risk of bias. It is likely that the majority of studies will fall into this category.

6. Other sources of potential bias

Low risk of bias

The study appears to be free of other sources of bias.

High risk of bias

There is at least one important risk of bias. For example, the study:

  • had a potential source of bias related to the specific study design used;

  • has been claimed to have been fraudulent; or

  • had some other problem.

Unclear

There may be a risk of bias, but there is either:

  • insufficient information to assess whether an important risk of bias exists; or

  • insufficient rationale or evidence that an identified problem will introduce bias.

Appendix 4. 'Risk of bias' assessment (cluster‐randomised trials)

In cluster‐randomised trials, particular biases to consider include: (i) recruitment bias; (ii) baseline imbalance; (iii) loss of clusters; (iv) incorrect analysis; and (v) comparability with individually randomised trials.

(i) Recruitment bias can occur when individuals are recruited to the trial after the clusters have been randomised, as knowledge of whether each cluster is an 'intervention' or 'control' cluster could affect the types of participants recruited.

(ii) Cluster‐randomised trials often randomise all clusters at once, so lack of concealment of an allocation sequence should not usually be an issue. However, because small numbers of clusters are randomised, there is a possibility of chance baseline imbalance between the randomised groups, in terms of either the clusters or the individuals. Although not a form of bias as such, the risk of baseline differences can be reduced by using stratified or pair‐matched randomisation of clusters. Reporting the the baseline comparability of clusters, or statistical adjustment for baseline characteristics, can help reduce concern about the effects of baseline imbalance.

(iii) Occasionally, complete clusters are lost from a trial, and have to be omitted from the analysis. Just as for missing outcome data in individually randomised trials, this may lead to bias. In addition, missing outcomes for individuals within clusters may also lead to a risk of bias in cluster‐randomised trials.

(iv) Many cluster‐randomised trials are analysed by incorrect statistical methods, not taking the clustering into account. Such analyses create a 'unit of analysis error' and produce over‐precise results (the standard error of the estimated intervention effect is too small) and P values that are too small. They do not lead to biased estimates of effect. However, if they remain uncorrected, they will receive too much weight in a meta‐analysis.

(v) In a meta‐analysis including both cluster‐ and individually‐randomised trials, or including cluster‐randomised trials with different types of clusters, possible differences between the intervention effects being estimated need to be considered. For example, in a vaccine trial of infectious diseases, a vaccine applied to all individuals in a community would be expected to be more effective than if the vaccine was applied to only half of the people. Another example is provided by a Cochrane review of hip protectors. The cluster trials showed large positive effect, whereas individually randomised trials did not show any clear benefit. One possibility is that there was a 'herd effect' in the cluster randomised trials (many of which were performed in nursing home, where compliance with using the protectors may have been enhanced). In general, such 'contamination' would lead to underestimates of effect. Thus, if an intervention effect is still demonstrated despite contamination in those trials that were not cluster‐randomised, a confident conclusion about the presence of an effect can be drawn. However, the size of the effect is likely to be underestimated. Contamination and 'herd effects' may be different for different types of cluster.